Referential communication in heterogeneous communities of pre-trained visual deep networks
Authors: Matéo Mahaut
Roberto Dessi
Francesca Franzon
Marco Baroni
Venue: Accepted by TMLR ()

Abstract: As large pre-trained image-processing neural networks are being embedded in autonomous agents such as self-driving cars or robots, the question arises of how such systems can communicate with each other about the surrounding world, despite their different architectures and training regimes. 
As a first step in this direction, we systematically explore the task of referential communication in a community of heterogeneous state-of-the-art pre-trained visual networks,  showing that they can develop, in a self-supervised way, a shared protocol to refer to a target object among a set of candidates. This shared protocol can also  be used, to some extent, to communicate about previously unseen object categories of different granularity. Moreover, a visual network that was not initially part of an existing community can learn the community's protocol with remarkable ease. Finally, we study, both qualitatively and quantitatively, the properties of the emergent protocol, providing some evidence that it is capturing high-level semantic features of objects.

Published in Transactions on Machine Learning Research (04/2025)
Referential communication in heterogeneous communities of pre-trained visual deep networks
Matéo Mahaut
sirieoo.mhaatteee edu francesco. framzon &upf edu roberto Supf edu
Francesca Franzon
Roberto Dessi
Universitat Pompeu Fabro.
Marco Baroni
marro.baroni@upf.coo
Universitat Pompeu. Fabro and ICREA
Reviewed on OpenReview:
Abstract
As large pre-trained mmage-processing neural networks are being embedded in autonomous agents such as self-driving cars or robots, the question arises of how such systems can com- municate with each other about the surrounding world, despite their different architectures and training regimes. As first. step this direction, we systematically explore the task of referential communication a community of heterogeneous state-of-the-art pre-trained visual networks, showing that they can develop, in self-supervised way, shared protocol to refer to target object among of candidates. This shared protocol can also be used, to some extent, to commmncctee about previously unseen object categories of different gran- ularity. Moreover, a visual network that was not initially part of an existing community can learn the community's protocol with remarkable case. Finally, we study, both qualitatively and quantitatively, the properties of the emergent protocol, providing some evidence that it capturing high-level semantic features of objects.
1 Introduction
As state-of-the-art vision-processing deep networks start being deployed as components of real-life au- tonomous or semi-autonomous systems, the question arises how such systems can communicate about the surrounding visual world, seen through the lenses of their respective core visual components. Consider for example two industrial robots produced by different companies, or two self-driving cars from different makers, that need to coordinate about objects in their enviromment. One might be powered, say, by a ResNet convolutional net (He et al. 2016) trained on the ImageNet visual recognition challenge (Russakovsky et al. 2015), whereas the other might use a Vision Transformer trained with a self-supervised algorithm (Dosovit- skiy et al. 2021 Caron et al. 2021). The set of object labels known to the two nets might differ (if labels are present at all), and, even when they coincide, the underlying categorization algorithms of the two nets might label the same object in different ways. Moreover, the original label set might not suffice to discriminate objects in the environment. For example, both nets might have been trained to categorize certain objects dogs, but the current situation requires them to distinguish among multiple dog instances, or to denote unfamiliar mammal neither network encountered at training time. Next, consider the scenario where a third system, relying on yet another visual network, added to an existing "community" of systems that are already able to communicate. Do we need to develop a new communication protocol, or can the new agent quickly adapt to the one already use? Could we eventually derive a universal protocol that any network- powered system could rapidly acquire? Note that, in the scenarios we are considering, the internal weights of the visual components might not be accessible for fine-tuning the network community single

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system, and, even if they were, this might be extremely costly undesirable, the core functionalities of the networks should not be altered.
We begin to tackle the challenges we outlined by studying whether a set of divense pre-trained visual networks, augmented with a light interface layer, can develop a shared protocol when faced with the core communicative task of referring to specific object among a set candidates (Skyrms, 2010). We are inspired by recent work on deep net emergent communication (Lazaridou & Baroni 2020). However, we adopt a different perspective with respect to this line of rescarch. Instead of designing ad-hoc architectures specifically optimized on the communication task, we look at emergent referential communication in communities of multiple networks with heterogencous architectures and training histories. We emphasize that our goal is to study the ability of heterogeneous networks to converge on a common referent. The full embodiment of such networks in autonomous agents that interact in a real-life environment involves a large number of challenges that we are not tackling here.
trained
After reviewing related work (Section 2 and presenting our general setup (Section we delve into our experiments in Section A First, in Section 1.1 we show that it is indeed possible for sets of heterogeneous pre-trained networks to successfully converge on referent through an induced communication protocol. In Section 4.2 we study referential generalization, showing that the developed protocol is sufficiently flexible that the norwsrks can use it to refer to objects that were not seen during the training phase (the "unfamiliar mammal" case we discussed above), as well as to tell apart distinct instances of the same object (the "distinguishing between different dogs" case above). We further show that the networks can successfully communicate about images from new datasets that were not used during communication training. In Section 4.3 we consider instead agent generalization. We show that, given community of networks that have been pre-trained network with different architecture, training objective or training corpus to acquire the existing community protocol, than for the enlarged community to develop a new protocol from scratch. This suggests that we could use a small set of networks to develop a "universal" protocol, that can then be taught in a supervised way to new networks that require the envisaged communication functionality. In Section 4.4 we offer some qualitative and quantitative insights into the protocol, suggesting that the network populations have developed a single shared code, that is not (only) referring to low-level image foatures, but also to higher-level semantic concepts, which we identify using sparse autoencoders (SAEs). The Conclusion (Section takes stock of what we found, and discusses the way ahead.
trained to communicate, it is faster for
The main contributions of this paper are as follows:
We introduce and motivate the task of referential communication between pre-trained heterogencous visual networks;
We show that a low-dimensional communication layer trained in a self-supervised way is sufficient to achieve high performance on the task, given a variety of different networks tested on in-domain data;
We show that the learned communication protocol partially transfers to out-of-domain data, includ- ing new and more granular referents than those seen at training time;
We show that new pre-trained network can successfully and elficiently learn existing protocol;
Quantitative and qualitative analysis of the protocol suggests that it is encoding high-level semantic features.
2
Related work
Deep net emergent communication While there is a long tradition of work on communication emer- gence in communities of generic computational agents (e.g., Cangelosi & Parisi 2002 Christiansen & Kirby) 2003 Wagner et al., 2003 Nolfi & Mirolli 2020 Stools 2012). there has recently been interest in the spe- cific topic of deep-net-based based emergent communication, with the aim to develop a protocol for autonomous information exchange between modern deep neural networks [Lazariouu & Baroni 2020).
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Since the ability to refer to specific object in a shared environment is seen one of the core building blocks of communication, many studies in this area have focused on the referential game setup, where a sender network must send a message to a receiver network to help it discriminate a target object among a set of candidates (e.g., Lazaridou et al. 2017: Havrylov & Titov 2017 Dessi et al. 2021
Inspired by human language, most work focusing on the referential game has been exploring a discrete communication setup in which the sender produces a symbol or sequence of symbols. On the other hand, contimuous channel is often preferred in scenarios where agents have to tackle navigation-like tasks in environments featuring relatively few distinct referents (e.g., Foerster et al.) 2016 Sukhbaatar et al. 2016) Ticleman et al. 2018 Kim et al., 2019; Singh et al.) 2019). Carmeli et al. (2022) have recently shown that continuous communication outperforms discrete communication in the context of the referential game. As discrete commuicattin imposes a non-differentiable bottleneck requiring special optimization techniques, we focus on the more natural continuous sctup. We do however explore alternatives involving a. discrete
channel in Appendix C
protocols (e.g., Raviv et al, 2019 Many studies have assessed the impact of community size in deep network language emergence
Larger human speaker communities lead to the emergence of
(o.g.. Tieleman et al. 2018: Cogswell et al. 2019 Graesser et al., 2019 Li & Bowling 2019 Ren et al.
2020 It appears that community-based training leads to desirable properties such as systematicity and ease of learning, at least when relatively small populations of homogeneous agents are considered. However, Rita et al. (2022) and [Chaboouii et al. (2022) have recently shown that, in large-scale setups and when appropriate controls are considered, population training by itself does not bring about any special benefit, We are not much interested in whether population-based training per se improves communication, but rather in whether it is possible at all to develop shared protocol that is usable by multiple heterogeneous pre-trained networks, and easy to learn for new networks added to community.
In typical population- basedd emergent communication studies, the networks in the population share the same architecture. Rita et al. (2022) looked at the effect of introducing sources of heterogeneity between senders and receivers, finding that using agents with different learning rates enhances population- protocols. While encouraging, this result was based on varying a single hyperparameter of otherwise identical networks, still far from our conmmuitt--o-hhterrgeoooo networks scenario.
Representation similarity, model stitching and multimodal representation learning Our work touches upon the issue of whother different neural networks share similar representations (c.g., Li et al. 2016 Morcos et al., 2018 MeNeely-White et al., 2020; Huh el. al. 2024).. . Some of the work on this topic
adopts method known "model stitchinngg " which consists in connecting intermediate layers of two models to measure their compatibility QLene & Vedaldi 2019 Bansal et al. 2021). This can be seen form of
model-to-model communcation. MMschella et al. (023;: Maiorca et al. (2023), in particular, have recently
introduced an unsupervised method that allows heterogeneous encoder and decoder networks to be "stitched" together in a 0-shot manner, based on second-order object representations (objects are represented by their similarities to a set of reference objects). Applying this approach to our referential communication scenario is a possible direction for future work. Research on multimodal representation learning and alignment (e.g.. Darvish et al. 2023 Girdhar al., 2023 Liang al.] 2023 Yu et al.. 2024) is also strongly related to our aims. For example, Moayeri et al. (2023) present cross- coos-visuul-mode alignment method similar to our communication layer. However, differently from their research line, where the emphasis is on multiple modalities and to train a single system that works across them, we are specifically interested in connecting heterogencous pre-trained visual models in a light and efficient way, and in studying the propertics of the emergent protocol shared by a whole population of such models.
Self-supervised lcarning for image classification Various self-supervised discriminative training ap- proaches have used objectives similar to our referential game, in order to derive classifiers without relying on annotated data (Chen et al.] 2020 Caron et al. 2021). Sometimes the setup is explicitly conceptualized as a multi-agent system (Grill et al. 2020p. Some of the questions that are discussed in this field are also relevant for us. For example Lavoie et al. (2023) studied the effect of sparsifying representations into a constrained space, showing it improves generalization new datatsets (we tried their methodology as an alternative way
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to derive discrete message representations in Appendix C but found it non-competitive in our setup). While there are similarities between our core methodology and self-supervised learning, our aim is fundamentally different: we are not trying to induce an image classifier from scratch without annotated data, but to let distinct pre-trained networks (typically already trained as image classifiers) share information.
Our referential game can also be seen as a content-based image retrieval task, and in this sense our work is related to image retrieval using contrastive objectives (e.g., Wang et al. 2020b; El-Nouby et al. 2021). However, do not train fine-tune full network to improve referent retrieval, but rather train shallow layers overlaid onto frozen networks with different architectures, and our emphasis, again, is on information sharing across networks.
Multi-agent systems More broadly, our work fits into the wider area of multi-agent systems research, specifically cooperative learning and the decentralized POMDP setup (Panait & Luke, 2005 Oliehoek & Amato 2016), a field in which there also interest in the problem of optimizing the behavior of diverse set of cooperating agents (e.g.. Canaan al. 2019). In the multi-agent decentralized learning domain, federated learning (c.g.. MMMMhan et al. 2017 Lim et al.
2020) focuses on leveraging distributed model Like us, this domain studies communication efficiency and convergence of multiple agents (Wang et al. 2020a). However, we do not distribute training among a set of similar networks, focusing instead on multiple agents characterized by strong differences, and their ability to complete a communication task.
instances with different data sources to improve training.
3 Setup
3.1
The referential communication game
Inspired by the core communicative task of reference (e.g., Skyrms 2010), the referential communication game a sender is given a target input (e.g., a picture) and issues a message (e.g., a word or, as in our case, continuous vector). A receiver is exposed to set of inputs, including the target, and must correctly point to the latter based on the message it receives from the sender.
Our sender and receiver are neural networks that include pre-trained and frozen visual modules, plus wrapper components in charge of message passing. We refer to the full interacting systems as agents. In each episode of the game, the sender agent receives a target image as imput, processes with its visual module, and outputs message the receiver. The receiver gets the message and is presented with set of candidate images, containing the target and series of distractors. For every candidate image, the receiver, using its visual module, constructs representation, which is then compared to a representation of the received message. The receiver selects whichever candidate has the most similar representation to the message. If the selected image matches the target, the episode is successful. Success above chance level is only possible if the receiver learns to correctly interpret the message sent by the sender. Pseudocode for the referential game is presented in Appendix]] A
Crucially, task accuracy only depends on target image matching, and does not require any kind of annotation. As such, the communication protocol entirely learned in a self-supervised way.
Below, we refer to the basic setup, in which a single sender-receiver pair is jointly trained to play the game, as the one-to-one setting, further distinguished into homogeneous and heterogeneous cases, depending on whether the two agents use the same or different visual modules for image processing. To extend the game to the population setting, given a population of senders and receivers, we randomly sample one sender and one receiver at each step to play the game as previously described.
3.2 Agent architectures and training
As mentioned, both sender and receiver are neural networks made up of frozen vision module and light feed-forward layers used for communication (Fig.
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Sender
Receiver
1
Vision module
Selection Module
2
Comm. Module
Message
3
Mapper Module
Vision module
Frozen parameters
Trainable parameters
3
2
Figure Referential game setup and agent archilectures. target image is input to the sender, that extracts a vector representation of it by passing it through pre-trained frozen visual network. This vector reprosentation is fed to the foed-forward Communication Module, that generates message consisting of another continuous vector, that is passed as one of the inputs to the Receiver. The receiver also processes each candidate image it gets input by passing it through a pre- tre-trained frozen visual network (which can have a different architecture from the one of the sender), obtaining set of vector representations. These are fod to the receiver's Communication Module, another food-forward component that maps them to voctors the same space as the sender message embedding. The Selection Module of the receiver simply consists of a parameter-free cosine similarity computation between the message and each image representation, followed by Softmax normalization. The receiver is said to have correctly identified the target if the largest value in the resulting probability distribution corresponds to the index of the target in the candidate array. Note that no parameters are shared between sender and receiver (except those of the frozen visual modules in the case in which the two agents are using homogeneous visual architectures).

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Architecture
Тype
Training
Parameters
ResNet152
CNN
Supervised ImageNet
60.2M
ResNet55
CNN
Supervised COCO
23.5M
Inception
CNN
Supervised ImageNet
27.2M
VGG 11
CNN
Supervised ImageNet
132.9M
ViT-B/16
Attention
Supervised ImageNet
86.6M
ViT-S/16
Attention
Self-supervised ImageNet
21M
Swin
Attention
Supervised ImageNet
87.7M
Table 1: Pre-trained visual architectures employed
3.2.1 Pre-trained vision modules
As vision modules, we use widely adopted networks that have freely downloadable parameters (Table I We mainly consider architectural variations of networks trained with supervised classification objective, this is the most common axis of variation across modern models. In particular, we use both convolutional (ResNet:
He et al. 20166. Inception: Szegedy et al. (0014), VGG: Simonyan & Zisserman 2015 and attention-based
networks ViT: Dosovitskiy et. al. (2021). Swin: Liu et al. (2021) ), trained on ILSVRC2012 ImageNet. data
RRusakovsky et al. 2015). We however also include our collection a ResNet (from Desai & Johnson (2021) that was trained on different dataset, namely COCO (Chen et al.) 2015). The latter involves fewer labels as well as stronger data imbalance (Gauen el. al. 2017]- Finally, we include an atlention-based network, DINO (Caron et al. 2021), that was trained on ImageNet, but using self-supervised objective (denoted by its VIT-S/16 architecture in Table [ Further pre-trained networks are used in the agent generalization
experiments, as briefly discussed in Section 4.3 below: see Table 22 in Appendix M for details.
The weights of the visual models are frozen (in blue in Fig, 1 and they are not modified at any point during our experiments. As output from the vision modules, we use their last layer, before the Softmax non-linearity is applied. Therefore, the vision module takes as input images and outputs vector representations.
While we interosted, by problem definition, in communication between pre-tmained networks, whose visual processing abilities largely derive from the pre-training phase, in Appendix B we try to assess to what extent emergent discrimination abilities are simple by-product of pre- vre-training. The results there suggest that this is not the case.
3.2.2 Trainable communication components
The second part of an agent's architecture consists in the layers devoted to handling communication. both sender and receiver, all commuicattion-reltted parameters are trainable, as shown orange Fig
In the sender, the communiation module is small linear food-forward layer, with between 6k and 264k parameters. For comparison, the frozen vision modules in Table D all have more than 20M parameters. The communication module takes as input the image representation, and outputs a continuous vector we refer to as the message emitted by the sender. We ran (Appendix D). Since we found a ceiling effect, we decided to run all experiments with both the smallest (16) and the largest (64) size that reached peak validation performance. Note that, when we use the term communication protocol below, we refer to the deterministic input-conditioned mossagc-cmitting policy the sender converges to after training.
search for message dimensionality
In the receiver, the vision module is followed by its communication module, mappping the former outputs to new, compressed image representations that live in the same space as the message. Following standard practice in the emergent communication literature, the message is compared to the image representations through the parameter-fie cosine similarity measure. The receiver communication module is larger than the sender communication component, being composod of two linear layers with hidden dimension, as we adopted the architecture of Dessi et al. (2021) without further tuning. The number of parameters remains well below that of the pre-trained vision modules. As example, VGG (132.9M parameters) is the vision

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module that has the largest output dimension, and therefore requires the largest communication module, with 8.4 million parameters, still less than 1/10 the number of parameters of the vision module.
Emergent communication research often adopts a discrete channel, as the latter more closely emulates human linguistic communication. We experimented extensively with a discrete interface, as reported in Appendix
C However, as discrete communication does not lead to clear advantages over continuous communication, and it involves more involved and brittle optimization process, we focus here on the continuous interface we just described.
3.2.3 Training
As the sender-receiver interface is continuous, the whole system can be straightforwardly trained by back- propagating the cross-entropy loss obtained by comparing the receiver output to the ground-truth larget.
Batch size is set at 64, the largest value we could robustly fit in GPU memory. As we sample distractors directly from training batches, on cach training step the referential game is played 64 times, once with every different image in the batch as target and the other 63 images serving as distractors. All experiments are implemented using the EGG toolkit (Kharitonov et al.) | All parameters needed reproduce our experiments with the toolkit (both the continuous setup reported here, and the discrete one discussed in Appendix C can be found in Appendix E Evidence of training convergence is provided in Appendix F in terms of gradient evolution profiles. Appendix G reports results for variants of training in which we used image augmentations and hard distractors.
3.3 Datasets
As nearly all agents rely on vision modules pre-trained on the ILSVRC2012 training set, we sample images from the validation data of that same dataset (50,000 images) to teach them to play the referential game, while reserving 10% of those images for testing (note that we do not use image annotations). Thus, our ImtgeNettk communication training and testing sets are both extracted from the original ILSVRC2012 validation set.
Agents are also tested on an out-of-domain (OOD) dataset containing classes from the larger ImageNet- 21k repository, as pre-processod by Ridnik et al. (2021). We selected the new classes among those that are neither hypernyms nor hyponyıss of ImageNet classes, and we made sure that no OOD class had a WordNet path similarity score above 0.125 with any ImageNet class 3 We morcover used number of heuristics, combined with manual inspection, to make sure the new classes were more or less the same level of gramularity as the ImageNetlk categories. After applying these filters, we were left with 52 classes denoting objects that belong to semantic domains disjoint from those represented in ImageNet such as trees (olive tree, fir, red pine) and human professions (carpenter, physician, organ grinder). For cach of them, we sampled all images available in the pre-processed ImageNet-211 dataset of Ridnik et al. (2021) (guaranteed to be at least 450). We used 90% of the OOD data for testing OOD communication accuracy (Soction 4.2 below) and to train the classificrs in the experiments reported in Appondix H The remaining 10% was used to test the latter classifiers
We further test the agents that were commuicctiion-rranee on ImageNetlk on 3 different datasets, two of which have very different image types from those present in ImageNet, namely: 1) Cifar100 (Krizhevsky ct al. ) which, like ImageNetlk, is made of images that tend to foreground single object; 2) Places205 (Zhou et al. 2014), focusing on scenery rather than objects; 3) CelebA: (Liu et al. 2015), whose images depict face close-ups.
ccm//aacabbokreeearcc//CCC scripts from our experiments are in //wgg//o// *As implemented in the NLTK toolkit: https://wwx .llkk.ak.... org/ Thee COCO datasct, used to pro-train one visual modules, contains four labels slightly above the threshold, with maximum similarity at 0.17.
hhtpps//gitthb.
We provide scripts to reproduce our ImageNetilk and OOD datasets httppp////itti...ccc///aaaaaaaaa

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Accuracy (16)
Learning Time (16) Accuracy (64)
Learning Time (64)
Homogeneous
99.3 + 0.8
9 + 8
98.6 + 2.9
6.5 + 8.1
Heterogeneous
95.3 = 3.4
7 + 3
99.2 + 1.1
9.7 + 6.5
Population 96.8 + 2.7
28
98.2 + 2.2
24
Table 2: Percentage accuracy and learning time agents playing the referential game. Learning time counts the number of epochs until maximum accuracy reached (bigger numbers indicate slower convergence). Results are reported for two message dimensions. The homogeneous row reports mean scores and standard deviations across pairs of same-architecture nets, each trained separately. The scores in the heterogeneous row are averaged across all 42 possible iffferent-architectur pairings, each trained separately. Population accuracy is averaged across all 49 possible net pairings after joint training. There is no standard deviation around population learning time as we have single jointly-trained population.
4 Experiments
We first compare single agent pairs and larger communities in the straightforward setup in which test images depict new instances of referents used for communication training (Section 4.1p. We then look at protocol
generalization along two axes: communicating about new referents (Section 4.2 and communicating with new agents (Section
4.1 Referential communication of homogeneous and heterogeneous networks
Starting from the baseline scenario in which there are only sender and one receiver with identical visual modules (one-to-one homogeneous setting), we look at the effect of pairing two heterogeneous architectures (one-to-one helerogeneous setting), and then scale up to training a full population of agents at once (popu- lation setting).
The results are presented in Table 2 We observe first that is possible to quickly train two homogeneous agents to near perfect referential accuracy (first row). Importantly, as shown in the second row of the table, pairing agents with different visual architectures only weakly affects accuracy and learning time, if at all. We conjecture that this ease of training in the heterogeneous setup is at least partially due to the fact that different architectures must ultimately extract comparable semantic features from images during pre-training, facilitating the task of making them communicate (although, as we will see below, there is no strong correlation between similarity of pre-trained visual module representations and communication performance). Detailed performance across all lested agent pairs is available in Appendix [ and we comment on some general patterns observed there in what follows. We focus here on agents trained with 16-dimensional messages, as with the 64-dimensional channel we are uniformly near the cciling.
Communication is easy for pairs that have different architectures. For example, communication between a VIT sender and a VGG receiver reaches 95% accuracy in 4 epochs. Communication is also easy for visual modules based on different training objectives: for example, ResNet-to-DINO communication reaches 98% accuracy in 12 epochs. It might look like communication is harder for pairs trained on different corpora, as 4/5 pairs requiring
or more epochs to train involve RosNet-COCO as the receiver agent. However, the latter also requires 29 epochs to converge in the homogeneous setup, suggesting that the (relative) issue with this module is the mismatch between its pre-training corpus (COCO) and the one used for communication training (lmageNetlk)) rather than its heterogeneity with respect to the other agents 5
It is also possible to train the full agent population to very high accuracy in relatively few epochs (Population row in Table 2 In this setup, we use all 7 modules as both senders and receivers, randomly pairing them at training time, ensuring each agent interacts with every other agent. A common communication protocol emerges that is mutually intelligible across all 49 agent pairings. Note that the large increase in learning lime compared to the one-Lo-one setups is only apparent, because during population
The somewhat larger average learning time and corresponding standard deviation in the homogencous 16-dimensional sctup compared the heterogeneous are accounted for by the extra time takes train the ResNet-COCO sender-receiver pair. Note that, during heterogeneous training, at least one the two agents always ImageNet-
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16-dimensional
Single-class
00D
Homogeneous
47.2 4 3.4
94.8 + 10.4
Heterogeneous
31.0 + 22.2
82.2 + 13.8
Population
34.7 + 6.8
72.2 + 44.8
Table 3: Percentage accuracy on ImageNet Out of Domain sets for 16-dimensional messages.
64-dimensional
Single-class
0OD
Homogeneous
47.4 + 6.4
90.5 + 8.0
Heterogencous
48.6 + 2.1
62.9 17.6
Population
36.5 + 8.6
72.2 + 13.8
Table 4: Percentage accuracy ImageNet Single-class and Out of Domain sets for 64-dimensional messages
training each agent is only seeing 1/7 of the data in each epoch Thus, in terms of actual data exposure, a joint count of 28 (or 24 when using the 64-dimensional channel) epochs correspond to 4 (3.3) effective epochs of data exposure for single agent, on average. Convergence thus faster on average population training than in one-to-one training.
We investigated whether the (generally small) differences in accuracy or learning time between agents with different visual modules is accounted for by the underlying discrepancy between their pre-trained visual representations. As different visual modules have different output dimension, we used RSA (Kriegeskorte jet al. 2008 to measure the correlation between visual module output similarity and accuracy learning time in communication training (measured for population pairs and one-to-one couplings, respectively). We did not find a significant correlation in either case, suggesting that communication training is quite robust to underlying visual representation differences.
4.2 Referential generalization
Having established with the previons experiment that it is possible to train a light communication layer that allows networks with heterogeneous architectures to successfully agree on a target referent, we go back to the questions about referent generalization we asked in the introduction. First, we explore whether the protocol make more gramular distinctions among objects coming from the same ImageNetlk classes that were used to develop it. For these purposes, we built special test set where all distractors are from the same ImageNetlk class as the target (so that, say, one target magpic needs to be distinguished from other magpies, instead of swans and bullfrogs). We tested all 1,000 ImageNetlk classes in smaller batches of 32 candidates, to ensure we had enough images in all cases. In this single-clas class setup, random baseline accuracy at around 3.1%. Note that, since we are interested in out-of-the-bon zero-shot generalization, we use the very same protocols induced with random distractors in the experiments above, without any special adaptation to the single-class setup.
Tables 3 and 4 show that communication is at least partially successful at more granular level than ImageNetlk classes, despite large drop from the random-distractor performance reported above. There is a noticeable discrepancy between 16 (Table 3 and 64-dimensional messages (Table 4 with the latter being generally better than the former, especially in the heterogeneous setup. We find this result intriguing, as we would have expected smaller message dimensionality act as a regularizing bottleneck, leading better generalization performance.
Next, we ask whether a communication protocol can also generalize, zero-shot, to referents that were not seen at training time. In particular, the systems trained on the referential game using IntgeNetlk data are now tested on the OOD dataset, that is entirely composed of new-class objects (see Section 3.3 above).
In other words, Table is comparing the scale the time it takes train 2 agents in the one-to-one setups to the cousider specific pairs of population
time it takes to train 14 agents in the population sctup. The difference is even sharper agents, at cach turn an agent is paired with one of seven distinct partners. In Appendix J show that, train the population for times more epochs, so that each population agent sees much data as agent the one-to-one settings, population accuracy further slowly increases, although it still generally lags behind that of one-to-one training.
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Cifar100
Places205
CelebA
Homogeneous
80.5 = 17.7
100 + 0.0
98.1 + 2.0
Heterogeneous
25.8 15.7
94.7 + 6.4
48.1 + 20.1
Population
27.8 = 44.8
85.2 + 35.5
38.3 + 48.6
Table 5: Percentage accuracy on unseen datasets (Cifar100, Places205 and CelebA) for 64-dimensional messages.
As Tables 3 and 4 show, compared to in-domain results (Table 2) there is a significant accuracy drop (larger in the more challenging heterogencous and population sctups), but performance remains good enough to suggest that the protocol is able to generalize to images depicting referents not seen during training well above chance level (recall that chance performance here at 1/64 ~ 1.6%). Larger dimensionality seems again to help generalization, at least in the homogeneous and heterogencous sctups.
Finally, we consider different kind of referent generalization, in which we take the agents that were communication-traneee trained on ImageNet, and test them on other datasets, that might contain very different referent distributions. In particular, Cifar100 focuses on a small number of concepts at higher level of granularity than ImageNet Places205 focuses on scenes rather than objects; CelebA is entirely composed of celebrity faces.
Table shows that 64-dimensional communication is still possible in this zero-shot dataset-transfer exper- iment (see Appendix K for the 16-dimensional case). With chance performance still at 1/64 1.6%, with Places205 we observe even higher accuracy than with the TmageNet OOD set. As shown in the examples of Fig. 3 and in Appendix [P] Fig. [6] this might be at least partially explained by the presence of familiar objects in the scenes depicted in this dataset. Performance on the CelebA dataset is near-perfect for homo- geneous agents, and comparable to that obtained on the single-class set for the other agents. This makes sense, as this face-only dataset can be seen as an extreme case of the single-class sctup. We conjecture that heterogeneous population pairs are less likely to converge on low-level features that might help catching the more granular differences of the single-class datasets. Finally, it is surprising that, while still much higher Cifar100, which is, like ImageNet, an object-centric dataset. By
than random, performance is lowest inspecting the Cifar100 images, we realized that they have much lower resolution than those of ImageNet, looking much blurrier. As we will see below in Section 4.4.3 the communication protocol is not robust to image blurring.
Results for 16-dimensional communication are presented in Appendix K with lower accuracies, especially in the homogeneous pair setup. We observe that the extra dimensions have low impact on in-domain performance (+ 2% for all three agent pairing setups), but are very useful for referent generalization in all cases, reaching a maximum of + 75% for homogeneous pairs in Places205.
They follow a similar pattern but
Overall, the main takeaways from the referent generalization experiments are the following. First, out-of- domain communication works out of the box with results way above chance, although there is a drop with respect to in-domain communication. Second, generalization is more successful when generalizing to new objects and scenes than when the agents must distinguish between objects of the same class
4.3 Agent generalization
Having tested how well the protocol can be applied new referents, we now consider whether is possible to efficiently extend it new agents, as in the practical scenario where a new autonomous device added ask whether new agent joining community of
to existing network of such items. In particular, agents already trained to communicate with each other can efficiently learn their communication protocol.
Note that, while the agents are trained ImageNet data, Cifar100 was used for the limited hyperparameter searches describe in Appendix [T)
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Figure Test accuracy and learning speed of learner agents (left: 16-dimensional messages; right: 64- dimensional messages). Blue line: learning curve on test data for learner agent added to communicating pair, averaged across all possible heterogeneous triples. Orange line: learning curve for a learner agent added to an existing community, averaged across all possible leave-one-out cases. Vertical bars indicate standard deviation across cases. As a baseline for learning speed, the dashed green line shows the learning curve when training the whole 7x7 population at once from scratch.
To Lest ease of learning of a communication protocol, we add single network, which we call the learner agent, to an existing community, either as sender or as receiver R This new agent has to play the referential game with the original agents and learn their protocol. To single out case of learning of the communication protocol itself, and not the original agents' adaptability, we freeze all communication layer parameters except for the learner agent's. The new communication layer therefore the only one being updated. The focus of this experiment is on the heterogeneous learners, where the new agent's vision module different from that of the original agents. We nonetheless observe that, in the homogencous case (not reported here), communication protocols are learned very quickly, needing at most one epoch to reach perfect near-perfect accuracy.
In the (heterogeneous) one-to-one setup, we train a communccttion protocol for each of the 49 possible different architecture pairs For cach pair, we then add a Icarner sender or receiver for every vision module not in the original pair (e-g., if the protocol was trained on the 5es-Se1152S pair, we lest, as both learner senders and receivers, Inception, VGG 11, ViT-B/16, ViT-S/16-DINO and Resnet50-COCO), In the population setup, the possible leave-one-out populations of different senders and 6 different receivers are trained the referential game, and, for each, the learner agent will use the 7th vision module that was left out, acting either sender or receiver.
Test accuracy and learning speed of the learner agents are reported in Fig. 2 In all rums with 64-dimensional communication channel, the learner agent succeeds at learning the communication protocol with accuracy of at least 90%. There some more variance with 16-dimensional messages, but in this case as well nearly all runs converge very high accuracy. Receivers learn better and faster on average (94%/959 995% for one-to-one with 16/64 16/64-messages, respectively, and 96%/97% for population) than senders (88% (88%/95% 995% and 95%/997% respectively). We don't have explanation for this, but we believe it is related to the fact that senders must perform more exploration when associating the target image to message in the emergent protocol, whereas the role of the receivers is more deterministic, in that they must just decode the sender messages (see Rita et al. 2020 for sender/recee /receiver learning asymmetries in emergent communication).
The comparison between the one-to-one and population setups reveals no clear winner: population accuracy is on average higher than one-to-one accuracy with 16-dimensional messages but lower with 64-dimensional
Alternatively, single pair of agents could initially be trained, and then all other agents could be sequentially added to the growing community. Appendix L shows that the all-at-once approach report here outperforms sequential population training.
9Thhse pairs also include homogenenus senders and receivers, but will always add heterogeneous agent the pair.
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messages. Population accuracy, however, is more stable in both setups, showing very little variance from case to case.
Regarding learning speed, agents that learn a pre-existing communication protocol reach high accuracy faster than it would take to retrain the whole population from scratch, as illustrated by the poptlation-from-cee
baseline in Fig. The communication protocol indeed initially very easy learn for new agent. There is no clear difference in learning speed between the one-to-one and population setups.
We further replicate the agent generalization experiments with other pre-trained models that had not been used at any point during our experiments so far: LeViT: Graham et al. (2021), CaiT: Touvron et al. (202] ResNeXt: Xie et al. (2017), DenseNet: Huang et al. (0117). Accuracies are above 95% from the first epoch for all settings (see Appendix MI, strongly confirming the generality of the protocol, and suggesting that, once the latter has boen developed, can casily be transferred to novel architoctures.
We also experiment with visual module that has been trained with (CLIP Radford et al. (2021)), ) since this might have introduced biases that makes it very different from purely visual models. We find that this is not the case. No drop in performance is observed for gencralization of CLIP induced protocols to new agents, or to new data: adaptation to this potentially outlying representation is not slower or less efficient. (see Appendix N
objective
4.4 Protocol analysis
Having shown its effectiveness, we turn now to a qualitative and quantitative examination of the emergent protocol. For this analysis, we focus on the overall beller-performing 64-ddimensional message selup.
4.4.1 Probing for degenerate communication
We begin by applying the "Gaussian blobs" sanity test introduced by Bouchacourt & Baroni (2018) We check whether the agents trained to communicate on the ImageNet1k dataset are able, at test time, to play the referential game on blobs of Gaussian noise instead of gemuine images. Success would mean the agents developed degenerate communication strategies, focusing on pixcl-lovel image fcatures that can transfer to the lask of discriminating between blobs of Gaussian noise, rather than on more desirable high-level semantic information. We find that all one-to-one and population-trainet pairs are indeed at chance level on Gaussian blobs, suggesting they are not just communicating about low-level image features.
4.4.2 Disentangling meaning with Sparse Autoencoder
To investigate which high-level features are used in communication, use dictionary learning, which has proven useful to identify interpretable features in multimodal models (Parekh et al. 2024), and more specif- ically Sparse AutoEncoders (SAFs) (Bricken et al., 2023 Cunningham et al. 2023 Rajamanobaran et al. 2024 Templeton et al., 2024 In our case, the SAE consists of singlo-layer encoders and decoders, with an added ReLu nonlinearity for the encoder. The encoder projects 64-dinnensional messages to an expanded hidden space (we use a hidden space of 16 x 64 1024 dimensions). Training is done with mean-square error reconstruction loss, and an L1 regularization loss to add sparsity (weight of the L1 term is set to 0.01). The sparsity component of the loss encourages storing meaningful information in individual dimensions. We train the SAE using messages corresponding to images from the communication training set (ImageNet1k, see section 3.3). Informally varying the SAE hyperparameters did not impact our general conclusions. We present SAE results for messages from a VGG 11 sender + ViT-B/16 B/16 receiver pair, which are represcntative of other cases.
First and most importantly, we observe that the dimensions uncovered by the SAE correspond to under- standable semantic features. In particular, we qualitatively assess this by looking, for each SAE dimension,
at the images corresponding to the messages closest to that dimension (T)) We observe that images clustered
around dimension share common human-interpretable meaning. In Fig. B which contains the images
10We were ahle do this for all 1024 dimensions hecause many of them share the same neighbors.
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Imagenet1k
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Figure 3: Top: ImageNet1k images which have messages closest to a chosen one-hot vector in SAE space. Bottom Left: CelebA images with messages closest to the same sparse dimension. Bottom right: 5 closest Places205 Images. No image from CelebA and Places205 is seen at training time by either sender, receiver or SAE.
closest to representative SAE feature, see different metallic grids. It is worth noting that they do not all share the same ImageNetIk class. The messages clustering around the same SAE vectors are abstract enough to overcome perceptual variation, with for example one of the images in Fig. g being photographed in night-time conditions (penultimate, first row), while the others all have good lighting conditions. We present additional representative examples in Appendix [ nature of the messages is provided in a qualitative error analysis in Appendix
(Fig. 11 Additional evidence for the semantic
SAF features also inform about referent generalization. We use the same sender and SAE trained on ImageNetlk to extract the k-nearest messages from the OOD datasets to each one-hot SAE vector. In general, the closest OOD messages to each SAE vector also form human-interpretable clusters, noisily sharing properties with the corresponding ImageNetlk training images. In the case of Places205, bottom right in Fig. 8 the images considered all contain an object with a metallic grid. For the more strongly OOD CelebA dataset, bottom left, pictures of celebrities tend to show either parallel lines, or in one case a metallic object. In general, OOD messages are futher away from one-hot SAE features than in-domain messages are, reflecting the fact that the corresponding images less closely match the properties captured by the features
From another perspective, instead of looking at which OOD messages are nearer pre-selected SAE vectors, we can ask which SAE vectors are closer to any message from an OOD dataset. Messages describing OOD images cluster around SAE vectors with properties relevant to the dataset. The CelebA messages tend to be closer to the SAE dimensions representing people, while Places205 messages are closer to SAE vectors corresponding to urban settings, or landscapes We provide an example of each in Fig. 12 of Appendix O
4.4.3 Perturbation analysis
In a next set of analyses, we gain insight on the information encoded in the message by looking at how image perturbations affect communication (an analysis of direct perturbation of the communication channel itsolf is reported in Appendix Q The way in which performance degrades in response to different perturbations should be informative about the image features that the communication protocol is relying on, and whether they are aligned with features that are intuitively crucial for own high-level characterization of images, These experiments can also be seen as further way to perform out-of-domain testing, where a protocol that was trained on non-augmented data is evaluated on perturbed images at lest time.
The effects of the perturbations on an example image are shown on the right in Fig, 4 We perturb images from the ImageNetlk datasct that were soen by the agents during referential game training, where the receivers have perfect accuracy. In each experiment, we apply the same transformation to all images, both
1n average, there are 56 ImageNetlk messages closer to feature than the closest CelebA message. For Places205, which is less OOD, there 12To avoid pareidolia, sponding cither feature close Places205 images, to feature corresponding to CelebA images, to neither. One of the authors then labeled them, without knowing their source, whether they contained foregrounded faces (close CelebA), depicted landscapes urban settings (close Places205), or neither. Performance was well above random chance.
average ImageNet1K messages closer feature.
verified this claim in small experiment. We prepared 30 scts of 10 ImageNetlk images,
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Figure 4: Accuracy after various image perturbations for the one-to-one and population setups (left). Gaus- sian blur was uniformly sampled within the [0.1,10] range. Mean accuracies and corresponding standard deviations are given across all 36 possible agent pairs. We add small horizontal offset to the accuracy values for different perturbations to make them all visible. The horizontal lines connecting one-to-one and population accuracies for the same perturbation are meant to case comparison between the two settings. Random baseline is 1.6% (randomly selecting one of the 64 images). Perturbation examples are provided on the right.
those seen by the sender and those seen by the receiver. Note that we test senders and receivers that have been trained with non-corrupted images, and only face the perturbations at test time.
Results for both the one-to-one and the population setups reported in Fig. 4 (left). All ablations affect accuracy. However, the color ablations (transforming the image to grayscale and jittering colors) and resizing the image are having a much smaller impact on performance than adding Gaussian blur. This is in line with our intuition about how much these changes should affect the ability to decode the contents of an image. As the examples on the right in Fig. | show, color and size ablations don't normally affect the human ability to discriminate the object depicted in the image, whereas blurring the image can easily make its contents difficult to decode for well.
We finally observe that all perturbation types affect one-to-one and population-trained agents in similar manner, with ippulaationtrrinee agents being marginally more affected 13
4.4.4 Specificity of population protocol
An important distinction in favor of population-based training is that in this setup, shown in Fig. 5 the senders converge to using very similar messages to denote the same image, approximating a single protocol. Note that the observation not trivial: differont senders could in principle have developed distinct protocols, with the receivers learning to process the union of these distinct protocols (we informally observed something of the sort taking place in the emergence of discrete population-baser communication). Fig. 5 also shows that the distance between one- mo--oo-ne senders, that have been trained independently of one another, is significantly larger average than that between conulation-trainel senders, and virtually indistinguishable from the baseline of averaging distances between messages representing different images for single sender.
To further illustrate, and build upon, the observation that population training has converged to a single protocol, in Appendix H we report an experiment in which the common representations induced in this setting are used for zero-shot classifier transfer.
18 we continue training the population we do Appendix the gap between population- trained and one-to-one agents eventually disappears.
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Figure 5: Distributions of cosine distances bel ween messages sent by two different senders for the same ImageNetlk training set images. The different senders are either trained in the heterogeneous one-to-one setup (blue) or in the same population (orange). As bascline, the green boxplot shows distances between the messages produced for different input images by population-trained VGG 11 and ViT-B/10 senders.
5 Conclusion
Coming back to the questions we asked in the Introduction, we found that is possible for agents based on pre-trained deep visual modules with different architectures, training objectives and training data to induce a successful referential communication protocol through a self-supervised training procedure that only involves light communication layer, without touching the visual module weights. The architectural extensions are simple and straightforward, suggesting that differing underlying pre-trained visual models already converged to similar reprosentations.
The networks can use the emergent protocol to communicate about referents that are only distinguishable at more granular level than used during communication training; and they can communicate about referents coming from categorics or even datascts that were not seen during pre-training nor in the communication protocol induction phase.
Importantly, we showed that a new deep net agent can successfully learn the protocol developed by an existing community, and that it can do so in less time than it would take to re-train the enlarged community including the new agent from scratch. This points to the possibility of developing "universal" deep net. protocol, that could be quickly taught to any new model before it is deployed in multi-network communication scenarios. We further found that the emergent protocol possesses a number of intuitive characteristics suggesting that it is focusing on semantic properties of the objects depicted in images, rather than on low-level visual features.
Despite our encouraging set of initial results, clearly much work still nceded before the envisaged commu- nication layer can be of practical use. First, observed a drop in performance in the referent, generalization experiments compared to communication about in-domain referents. Future work should further explore the generality of the protocol, and devise ways to broaden it.
We have molivated our study in terms of nets embedded in robots and self-driving cars. Such systems will not perceive the world as a discrete set of pictures, but rather as a fully embodied egocentric image flow. This scenario obviously poses further challenges. For example, two agents that need to communicate about an object would almost never have an identical view of it. Scaling up our approach this setup is another important direction for future work.
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Referent denotation is an important building block of communication, but it's clearly just the starting point, and further work should extend deep net communication to other functions of language, such as highlighting different properties of objects, describing actions, and distinguishing between providing information, asking for it and giving instructions. These features will in turn require extending the setup to multiple exchanges, and agents that can function both as senders and as receivers.
Acknowledgments
We thank Agnieszka Slowik for early contributions to this work. We are also grateful to Thomas Brochhagen, Dan Dediu, Rahma Chaabouni, Emily Cheng, Valentino Maiorca, Luca Moschella, Clément Moulin Frier, the members of the COLT group and the TMLR editor and reviewers for useful feedback. This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No. 101019291). We also received funding from the the authors' view only, and the funding agencies are not responsible for any use that may be made of the information it contains.
Catalan government (AGAUR grant SGR 2021 00470). This paper reflects
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A The one-to-one one referential game in pseudocode
initialize sender and receiver for training iterations do
for batch of images b in training data do
recdists = list()
groundtruths = list()
for image i in b do
grounduruuhssaabl((n--noo vector with at i's index)
sender receives i
sender outputs message vector receiver receives message and b receiver processes message and each image in b to generate representations reedist = cosine between receiver representations of message and images in b reedist = softmax(rocdist) recdists.acdd(recdiss)
end for
compute cross-entropy loss CE between groundtruths and recdists backpropagation update
end for end for For the images in a batch, the cross-entropy loss function computed as follows:
CE Σ
reedists;j log(groundtruhhss))
In the population-base setup, all available senders and receivers are initialized at the beginning, and a sender and receiver randomly selected at each iteration of the second for loop.
B Using pre-trained vision modules without communication layer
Our setup assumes that need to connect networks that have already been pre-trained visual tasks. An interesting question is to what extent the communication layer is adding something non-trivial to what is already encoded in the pre-trained visual represenlations of the networks. To assess this, we design version of the communication game where the agents are pre-trained visual modules without any communication- specific fine-tuning.
Specifically, given two (distinct) pre-trained models that can predict image labels, we play the referential game follows:
sender predicts label of target image;
receiver predicts label of all candidate images;
sender and receiver labels of target differ, receiver picks random image;
labels coincide, the receiver picks a random image among those for which has predicted the target label;
the game round is successful the receiver picks the target image.
We experimented with all the models that were trained with the same label set (VIT-B/16, Inception,
ResNet152, VGG11, Swin), using 10k images for each dataset. The results in Table 6 The models
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still communicate above chance level, but clearly communication layer training brings about big improvement (compare to tables and above), successful referential communication across models confirming that this process is important to ensure
Dataset
Percentage accuracy
ImageNet (Val)
58.3 + 21.3
ImageNet (OOD)
16.1 + 4.3
Cifar100
9.3 + 10.0
Places205
24.4 13.0
CelebA
10.4 5.2
Table 6: Percentage accuracy playing the communication game without trained communication layer across various datasets, reported as mcan + standard deviation over all heterogencous pairs.
C Discrete experiments
Building intuitions from the deep net emergent. communication literature, we studied whether imposing general protocol than direct communication through a
discrete bottleneck would lead to a better, implemented discrete bottleneck letting the agents exchange
continuous channel. In a first experiment, one-hot vector, by optimizing it through the widely used Gumbel Softmax differentiable relaxation (Havrylov & Titov 2017 Jang et al. 2017 Maddison et al., 2017), fixing vocabulary size (dimensionality of the message vector) to 256. Note that, when communication
discrete, a further decoder component added to the receiver architecture, mapping the discrete messages back to the continuous space in which it can be compared the candidate image representations.
We did not find any evidence that the discrete bottleneck helps. Not only is the discrete protocol signifi- cantly less accurate than the continuous one (Table 7) but this low performance hides any trace of better generalization capabilities (Table 8
When generalizing a discrete communication protocol to a new agent (Fig. as done for contimuus messages in Section 4.3 we observe convergence to a lower accuracy at a slower pace. Still, this setup confirms even more dramatically that less time required train new agent top accuracy than is needed retrain a larger group from scratch. Specifically, the population trained from scratch would require 23 epochs to reach its best accuracy, whereas 5 epochs are sufficient to teach new agent an existing protocol. The population training paradigm is more stable across architectures, as with continuous messages.
In order to make sure that our negative results concerning discrete communication are not due to an unfor- tunate choice of hyperparameters, we repeated most experiments across a range settings. We report here results for the more intercsting population sctup, but similar patterns are observed in onc-to-onc training. Table shows that there small increase in accuracy when enlarging vocabulary size (compared to the results obtained with 256 -56-dimensional messages reported in tables and above), but performance is still well below that of the continuous case even when using vector large 8192 dimensions (compared to
For technical reasons, were not able to reproduce the Single-Clase experiment for this setup. We expect however that performance would be extremely low for models that have been trained to predict the same label for all images presented in the same candidate set, as is the case with the Single-Class experiment.
Accuracy
Learning Time
Homogeneous
78.2 + 0.1
20 + 1
Heterogeneous
71.1 + 4.3
22
+ 2
Population
62.4 + 3.2
23
Table 7: Percentage accuracy and learning time of agents playing the referential game with discrete messages once both agents have reached convergence. Terminology as in Table
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Single-class
Out of Domain
Homogeneous
13.2 4.1
43.2 + 5.0
Heterogeneous
16.0 6.2
29.1 4.3
Population
8.9 + 2.1
25.8 + 4.5
Table 8: Percentage accuracy on single-class and OOD test sets for discrete communication
1.0
0.9
0.8
0.7
Accuracy
0.6
0.5
0.4
0.3
0.2
0.1
3
4
Epochs
Figure 6: Test accuracy and learning speed of discrete learner agents. Blue line: learning curve on test data for learner agent added to a commuicating pair, averaged across all possible heterogeneous triples. Orange line: learning curve for a learner agent added to an existing community, averaged across all possible leave-one-out cases. Vertical bars indicate standard deviation across cases. As baseline for learning speed, the dashed green line shows the learning curve when training the whole 7x7 populations at once from scratch.
16 or 64 dimensions in the continuous case). Note that using vocabulary sizes below 256 (not shown in the table) led to failure to converge to non-random accuracy.
We also considered replacing Gumbel Softmax optimization with the commonly used policy gradient RE- INFORCE algorithm for fully discrete optimization (Williams 1992). Gumbel Softmax (Table proved be the more effective training strategy. Switching to REINFORCE (Table 10) causes accuracy to drop to near-chance levels.
REINFORCE allows a straightforward implementation of multi-symbbo autoregressive communication, where we let the sender and recciver produce/pant parrec parse symbol sequences by replacing their communccttin encod-
Vocabulary size
ImageNetlk
OOD
512
62.1
26.8
1024
66.8
31.1
2048
71.7
32.1
4096
74.3
33.4
8192
78.5
35.2
Table 9: Percentage accuracy of agents evaluated on the two ImageNet- -bsss test sets in function vocab- ulary size. Agents were trained with Gumbel Softmax in population setup.
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Vocabulary size
ImageNetlk
OOD
256
5.6
3.9
512
3.4
2,4
1024
3.3
2.1
2048
2.8
1.6
4096
1.8
8192
2.2
2.1
Table 10: Percentage accuracy of agents evaluated on the two ImageNetlk test sets. Agents were trained with REINFORCE in population setup.
Image/Net1k OOD
Homogencous
84.4 + 15.4
63.9 + 17.7
Heterogeneous
82.2 + 5.9
45.6 + 12.3
Population
62.7 + 3.4
24.9 11.1
Table 11: Percentage accuracy of agents evaluated on the two ImageNetlk test sets. Agents were trained with simplicial emboddings.
ing/decoding components with recurrent LSTM network (Hochreiter & Schmidhuber 1997). We set maximum message length to 10 symbols. Performance of this multi-symbol approach stays capped at 1.6% accuracy, which corresponds to the random baseline.
We conjectured that the overall disappointing performance of discrete communication due to the difficulty of optimizing agent coordination when the error gradient cannot be passed through the discrete channel. We thus tried the simplicial embeddings from Lavoie et al. (2023), this approach results in nearly discrete representations while not requiring discrete-channel optimization. We applied the method on top of the continuous linear layer, which maps the vision module representation space to message space (16 dimensions). We cut the 16 dimensions into 2, 4 or 8 groups of 8, 4 or vectors respectively, and apply a softmax non-linearity within those groups, before concatenating everything back to 16 dimensions. The number of groups had very little impact on results, and led to the same average performance. Accuracy with both in-domain and out-of-domain datasets is reported in Table II and it remains below that of plain continuous communication, without the expected boost in generalization abilitics.
If using a discrete channel is considered priority (for example, because it might lead to more interpretable protocol), other discrete optimization techniques could also be explored, such as more advanced policy gra- dient methods Francoos-Lavet et al. 2018), or quantization approaches such as the one recently proposed by Carmeli el al. (2022) (although we tried something similar, with -ens-hhin-nncouraging results, in Appendix
Hyperparameter search for continuous communication
Since, following standard practice, we are using the ILSVRC2012 validation partition for testing (the ILSVRC2012 test partition is not available), we rely on a separate dataset, namely Cifar100 Krizhevsky et al. 2009), for hyperparameter tuning.
The two main hyperparameters to consider in continuous communication are the size of the continuous message, and the non-linearit applied to it. We performed multiple grid searches to determine the best and most efficient combination, through referential game experiments run on Cifar100. Table 12 shows accuracy at epochs 1 and 25 of an agent pair based on the Inception vision module.
On our validation set, all dimensions between 16 and 64 reach perfect accuracy. We thus decided to run the main experiments with both the smallest (16) and largest (64) dimensionalitics this range. We leave it future work to explore even larger dimensionalitics. We remark, however, that one of our desiderata is to keep the communication module small, and increasing dimensionality inevitably expands the size of the
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Non-linearity
Sigmoid
Softmax
Dimensions
Epoch
Epoch 25
Epoch
Epoch 25
5.8
14.1
13.4
12.0
40.2
73.8
32.6
67.3
8
97.1
99.7
94.2
98.7
16
100
100
100
99.9
32
100
100
100
100
64
100
100
100
99.8
Table 12:
Grid search reporting percentage accuracies for different message dimensions and non-linearities in an Intppppoooto-tcpttoon referential game on Cifar100 data.
Hyperparameters
batch size optimizer learning rate
64
Adam
1c-4
max message length
non lincarity
sigmoid
vocab size
16 / 64
receiver hidden dimension
2048
image size
384
receiver cosine temperature
0.1
Table 13: Hyperparameters for training continuous communication channels.
module. We also note here that dimensionalities smaller than 16 still manage carry lot of information. We are above the random chance level of 1/64 1.6% even with 2 dimensions.
The sigmoid non-lincarity has a small margin over softmax (clearer in the smaller dimensions, where there is no ceiling effect), and we thus pick the former for the main experiments.
E
Experiment hyperparameters
All hyperparameters required to reproduce the continuous (main-text) experiments using the EGG toolkit are provided in Table 13 Hyperparameters for discrete experiments (Appendix [ are in Table 14 Table
15 has hyperparameters for the REINFORCE experiments (Appendix Ch. Each experiments was conducted
using a single NVIDIA A30 GPU.
Hyperparameters
batch size
64
optimizer
Adam
learning rate
1c44
max message length
vocab size
8192
receiver hidden dimension
2048
image size
384
gumbel softmax temperature
5
gs temperature decay
1
update gs temp frequency mimimum gs temperature receiver cosine temperature
1
0.1
Table 14: Hyperparameters for training discrete communication channels using the Gumbel Softmax.
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Hyperparameters
batch size
optimizer
Adam
learning rate
le-4
message length
receiver hidden dimension
2048
image size receiver cosine temperature
384
0.1
sender entropy coef
0.5
KI. divergence coeff
Table 15: Hyperparameters for training discrete communication channels using REINFORCE.
sender receiver, nom::
100
LO05
2000
3000
4000
Learning step
Figure 7: Higher gradient norms result in bigger optimizer steps. Despite being in multi-agent setup, both agents stably converge. Receiver and sender lines the plot averages across all possible continuous one-to-one experiments.
F
Gradient evolution during training
We verify convergence by providing evolution of the loss of individual continuous learners throughout training in 7 (the same is true for discrete learners, around 4 times slower), also finding that receivers go through higher norm weight modification than senders during the initial stages of learning, as previously noted by Rita et al. (022).
G Augmentations and hard distractors
We consider two training strategies commonly used in self-supervised learning: image augmentation and hard distractors. Dessi et al. (2021) showed that augmentations are crucial develop non-degenerate protocols when training deep agents from scratch communcatte. We faithfully follow their data augmentation pipeline, using the same hyperparameters. We stochastically apply crop-and-resize, color perturbation, and random Gaussian blurring to every image.
Results for agents trained with data augmentations are presented in Table 16 In all experiments, including those that require generalizing out-of-domain, the agents trained with augmentations perform considerably worse than those trained without augmentations (compare to Tables 2 and 3 and in the main text). We hypothesize that since, unlike Dessi et al. (2021), we are using pro-trained visual models that already abstract away from low-level features of the images, there is not further benefit in applying augmentation during the communication-trainiie phase. At the same time, this phase only tunes single layer, which implies that
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ImageNettk
Single-class
0OD
Homogeneous
90.2 + 6.7
30.4 + 8.2
61.2 + 13.7
Heterogeneous
88.7 + 5.1
30.2 + 2.3
53.1 + 13.3
Population 78.8 + 40.8
19.3 + 39.5
41.1 + 49.2
Table 16: Percentage accuracy of agents evaluated on the ImageNet1k test sets (64-dimensional communi-
cation channel). Agents were Irained with data augmentation applied to sender and receiver inputs. No augmentation is applied at test time
ImageNetlk
Single-class
OOD
Homogeneous
99.9 + 0.2
47.6 + 2.1
97.0 2.2
Heterogeneous
97.1 = 3.8
35.1 + 9.6
68.0 = 18.5
Population 63.7 + 48.1
14.2 + 8.5
30.9 + 46.2
Table 17: Percentage accuracy of agents evaluated the ImageNetlk-basee test sets (64-dimensional com- munication channel). Agents were trained on batches of 64 images with low cosine distance.
the models do not have a lot of power to adapt to the augmentations. This ends up making the task harder without bringing about better abstractions skills than those already acquired during pre-training.
Another interesting idea we can borrow from self-supervieed contrastive learning is that of choosing hard distractors at training time (Robinson et al. 2021). As we do not want to rely on data annotation, use the cosine distance between pixel matrices of a target image and the other images in the training set to find near distractors (we manually checked that this simple strategy does provide intuitively similar distractors). We create batches by randomly picking an image the Imagenetlk dataset, and adding the 63 closest images. We repeat the process throughout training. Results are found Table 17 While agents succeed in learning communication protocols to the same degree as in the original setup, they do not outperform it. In the Single-class case, where could expect improvement from training on similar images, heterogencous pairs even perform slightly worse. We thus adopt the simpler and faster random batching approach for all experiments in the main text.
H Exploiting the communication layer for zero-shot classifier transfer
Besides our primary goal to study communication between heterogeneous networks, a learned shared protocol is appealing from a representation learning perspective [Tieleman et al. 2018). We illustrate the point here by showing how, through this independently learned representation, we can perform seamless zeru-shol classifier transfer, task that is related to recent literature on "model stitching" (see references in the Representation similarity and model stitching paragraph of Related Work (Section 2 of the main article).
We consider set of heterogeneous sender agents that have been population-trained to communicate on the ImageNetlk dataset. A single sender from the population is sampled and its output messages (64-dimensional vectors) are used to train a linear classifier on the OOD dataset classes. All agents reach good performance (Table 18 despite the fact that neither the communication layer nor the visual modules are further tuned to recognize the new out-of-domain classes.
The very same classifier then transferred, without any further tuning, another sender, and its OOD classification performance is evaluated. As shown in Table I8 the transfer senders, despite never having been paired up with this classifier during training, reach accuracy comparable to that of the classifier tested with the sender it was trained on (in several cases, the transfer senders even outperform the original senders, possibly thanks to the quality of their visual components).
This positive result confirms that population-traininn leads to a single protocol shared by all senders, and it points the usefulness of the communication layer as a compact shared representation for visual deep nets opportunity to be further explored in future work.
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Vision Module
Accuracy
Transfer Accuracy
VGG 11
76.7
87.6 + 2.1
Inception
84.6
89.5 + 3.0
ResNet 152
85.2
82.8 + 7.3
VIT-B/16
79.84
85.6 + 6
ViT-S/16 DINO
85.1
88.5 3.0
Swin
81.8
88.7 2.8
ResNet50 COCO
90.4
87.3 1.9
Table 18: Percentage accuracy of linear classifier trained to categorize objects in the OOD dataset using communication layer representations as input. Transfer accuracy is the average percentage accuracy for the same classification task when directly transfering the trained classifier to another agent from the population.
Population Accuracy
Learning
1.00
400
0.96
0.38
0.38
0.36
0.96
0.34
6
-25
swii rossc ranue. inap
0.99
0.99
0.98
1
0.99
0.90
0.96
6
22
12
0.96
0.32
0.96
0.95
0:93
0.01
0.94
15
8.95
0.97
0:91
0.91
8.99
8.85
0.90
0.83
0.54
0.95
0.88
0.86
0.56
0.99
0.80
incep
IMAIM
MMI
vit
dime
intep
acaca
swin
sit
dirn
Figure 8: Pairwise population communication accuracy and heterogencous one-to-one learning time for every visual module (16-dimensional messages). The axis denotes senders, the x axis receivers. A line separates CNNs from attention-based architectures.
Visual-module to visual-module analysis
In order to ensure that the high accuracy observed terms averaged population performance does not hide weaker communication for specific pairs, we checked every pair of agents' performance, and recorded them Fig. g We present the accuracy matrix for the population setup when using 16-dimensional messages, since it is the one where observe in general more variance from pair to pair, compared to the heterogeneous one, and to using 64-dlimensional messages, where we observe a ceiling ellect.
Communication accuracy is consistently high, even across different architecture types (CNNs: VGG11, Inception, ResNet, ResNet-COCO, left of the line; attention-based Swin, ViT-B and ViT-S (DINO), right of the line) or different. training paradigms (DINO was self-supervised, ResNet-COCO was pre-trained on the COCO dataset, while all other agents used supervisod pre-training on the ILSVRC2012 ImageNetIk dataset). The lowest accuracies are observed when pairing ResNet-COCO (a CNN pre-trained on COCO) with either ViT-B DINO, two attention-base architectures, suggesting compounding effect for differences both in architecture and pre-training corpus.
Concerning training lime, we report in Fig. data for the heterogeneous one-Lo-one setup (population time is 28 epochs). Training times are generally low, with the two outliers involving ResNet-COCO. Note that one of these outliers actually the homogenous are observing main effect of wrrraannnnnommmmmttnnnnnnttntnie training corpus discrepancy, rather than issues arising when combining heterogeneous agents.
case, suggesting that we
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J Longer population training
We let 64-dimensional population training run for 7 times the number of epochs (175 epochs) of one-to-one pair training, so that each single agent in the population is updated as often as the one-to-one homoge- neous/heterogeeneus agents. Results are in Table 19
Dataset
Accuracy
ImageNet (Val)
99.2 + 1.1
ImageNet (OOD)
78.0 + 15.3
ImageNet (Single Class)
43.6 + 5.0
Places205
92.8 + 6.7
CelebA
45.2 + 24.8
Cifar100
37.6 + 27.5
Percentage accuracy for population with longer training across all different dalasets. Values are reported as mean + standard deviation across models in the population.
Table 19:
While performance appeared plateau, if we keep Training, we observe instead non-negligible improvements across experiments (compare to tables 2 3 and 5 above). Still, in all cases except for the in-domain ImageNet set, performance still lags behind that of the heterogeneous pairs, that were trained in 1/7 of the time required to bring population performance at the level reported here.
K 16-dimensional protocol generalization to unseen datasets
We repeat the experiments reported in Table 5 with the 16-dimensional communication channel. The results (in Table 20) show that communication always remains much above random chance (1/64 = 1.6%) all three datasets. Nonetheless, the larger communication channel leads to betler performance out of distribution, with up to 75% decrease of performance for the 16-dimensional messages in the setup where homogeneous pairs generalize from ImageNetlk to Places205.
L Sequential population training
In the main paper, we train population agents in parallel, by pairing random sender and receiver from the whole population at each step. We explore here the alternative in which we initially train pair of agents for number of epochs, and then progrossively let the community grow one agent time.
Two important hyperparameters of sequential population training are agent order and training schedule, namely how many epochs are given to each new agent before introducing the next. Concerning agent order, we tried 10 random sequences, and we report results averaged across them. Concerning training schedule, we considered two setups. In the busic experiment, we trained the first pair for 2 epochs, and then trained for epochs more after adding each new agent (sender or receiver), for total of 30 epochs. This is comparable to the total amount of training of the population used for the main experiments (25 epochs). In the long experiment, train the initial pair for 7 epochs, and then, for each agent introduce, train for 7 more epochs. This is based on the observation that pairwise convergence tends to take 7 epochs or less. Tn
Cifar100
Places205
CelebA
Homogencous
23.39 + 2.1
25.0 + 0.0
24.91 + 0.15
Heterogeneous 11.78 + 5.04
24.20 + 0.01
15.67 + 5.02
Population
11.09 31.40
23.51 + 42.41
14.19 + 34.89
Table 20: Percentage accuracy on unseen datasets (Cifar100, Places205 and CelebA) for 16-dimensional messages.
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this case, rumning total of 105 epochs, closer to the training length of the experiment report in Appendix J above (175 epochs). For both setups, adopt 64-dimmnsiooall communication vector.
Dataset
Basic
Long
ImageNett (Val)
94.5 + 9.2
98.1 3.1
ImageNeet (OOD)
45.0 = 22.7
72.1 = 18.2
ImageNet (Single Class)
32.6 + 11.1
38.4 + 8.8
Places205
63.6 + 26.0
91.1 + 8.3
CelebA
33.4 + 23.1
40.0 + 25.2
Cifar100
27.4 - 25.3
35.6 = 27.7
Table 21: Percentage accuracy for two sequential training lengths on all datasets. Values reported mean + standard deviation across sequence orders.
In both selups, sequential training leads to results that are inferior to those of the corresponding parallel- training experiments: cf. tables 2 and 5 in the main text for the basic results and Table 19 in Appendix for the long results.
We conjecture that the problem with sequential training is that the first agents to be trained will dispropor- tionately affect the emergent protocol, making harder for later agents to adapt. Given also that sequential training requires more hyperparameters to tune, the takcaway here is that parallel training is the better option.
M Additional agent generalization with completely unseen models
We extended agent generalization to a set of networks that were not seen in any previous experiment or hyper-parameter search (Table 22
Fig. shows average epoch-per epoch-accuracy of those new agents, learning protocol created by the original agents. Agents learning fixed communication protocol from pair (blue) or population (orange) of agents. These new learners (mostly based architectures more recently developed than the ones considered in the initial experiments) learn the protocol developed by the original agents faster and to higher accuracy, with less variance despite having a comparable number of weights.
N Using CLIP visual module
We rerun all experiments adding the CLIP image encoder Radford et al. (2021) to the set of visual modules. Unlike the other networks use, CLIP has been intentionally trained produce visual representations that are aligned to verbal descriptions of the corresponding image contents. Images and their captions encoded by different components, which are trained to have high similarity between their final representations, that image representations end aligned with text captions. Our version of CLIP uses the ViT architecture for image encoding (we use the ViT based clip model from the 'PyTorch Image Models library (Wightman, 2019), in it's version adapted to images of size 384x384 pixels with 16-dimensional patches).
Architecture
Type
Training
Parameters
ResNeXt
CNN
Supervised ImageNetlk
25.0M
LeViT
Attention
Supervised ImageNet 1k and distillation
39.1M
CaiT
Attention
Supervised ImageNet1k ImagenetV2
271.2M
DenseNet
CNN
Supervised ImageNellk
28.7M
Table 22: Pre-trained visual architectures employed in the further agent generalization experiments. LeViT uses distillation from a model which was also trained on ImageNetlk. CaiT trained on ImageNetV2.
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0.7
0.5
0.4
trainingg type
Gne-lo-one learner Population Population
0.3
Ppochs
Figure 9: Test accuracy and learning speed unseen learner agents (LeViT, CaiT, ResNeXt and DenseNet) with 64-dimensional messages. Blue line: learning curve on test data for unseen learner agent added to communicating pair of agents from previous experiments (ResNet, ViT, VGG, Inception, DINO, Swin, ResNet COCO), averaged across all possible heterogeneous triples. Orange line: learning curve for unseen learner agent added to existing community, averaged across all possible pre-trained agent populations. Vertical bars indicate standard deviation across cases. As bascline for learning speed, the dashed green line shows the learning curve when training the whole original 7x7 population at once from scratch.
ImageNetlk OOD
Single-class
Cifar100
Places205
CelebA
Homogeneous
1.0
99.83
99.46
99.98
99.94
Heterogencous
52.70 + 10.11
35.53 + 9.98
44.38 + 28.34
80.70 + 11.91
28.28 = 17.82
Population
62.43 + 48.43
46.77 + 49.90
30.35 + 45.97
84.53 + 36.15
30.85 + 46.19
Table 23: Percentage accuracy OOD datasets for pairs including a CLIP model.
Despite strong change in training paradigm and the alignment to human language, which is different communication protocol, results do not vary noticeably from the main results. For ImageNetlk communi- cation, homogeneous, heterogeneous, and population setups all perform above 95% accuracy. Results are also similar to those of unimodal agents for both referent generalization (Table 23 and agent generalization
(Fig. 10
Further SAE feature examples
Fig. shows SAE features that seem to represent, respectively, 1) cups, glasses and other food containers 2) airplanes and 3) transportation and large animals. As in Section 4.4.2 the features correspond to one-hot vectors in SAE space, and we capture their semantic content by looking at the ImageNetlk images whose messages are the nearest neighbours these vectors. Both the container feature and the airplane feature seem coherent, with little variation in the nature of the images closest. the feature. For the pansportation/lagg animals fcature, while it understandable that vehicles and metro stations cluster togethor, it is less clear why large animals would also be encoded in the same feature. A tentative explanation provided by the 9th image, where a horse and its carriage appear together. This suggests that large animals are included in this feature because they can be means of transportation, too. Alternatively, this is another example showing that SAE features are not perfectly monosemous.
Regarding the OOD images, Places205 images in the figure also tend to have a central foregrounded object, much like the ImageNet1k ones. Taking the example of the airplane feature, the closest Placos205 images all have an airplane in the center of the image, exactly like the ImageNet 1k images. the case of the container and the transportation/Hene /large animals features, images from the Places205 dataset visually more distant,
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0.0
0.4
Training lype
One one learner
Population
0.31
Population wenatch
Epochs
Figure 10: Test, accuracy and learning speed of learner agents with 64-dimensional messages, when CLIP is added to the agent community. Blue line: learning curve on test data for learner agent added to a communicating pair including CLIP agent, averaged across all possible heterogeneous triples. Orange line: learning curve for a learner agent added to an existing community, averaged across all possible 7 pre-trained agent populations (6 populations with CLIP, and one without, where CLIP is the learner). Vertical bars indicate standard deviation across cases. As a baseline for learning speed, the dashed green line shows the learning curve when training the original 7x7 population (no CLIP) at once from scratch.
but contain relevant objects with clear semantic link, such plate for the container feature, bus seats for transportation. For this dataset, agents seem to strongly rely objects present in the scene, much like in the Imagenet case. On the other hand, the CelebA dataset results are more difficult to interpret. Celebrities within a feature appear to share common traits. For example, for the container feature, they all have black hair, and black accessories (either glasses, a hat, or a tuxedo). For the plane feature, most of them wear some kind of headgear. Despite this internal coherence, linking these pictures with the meaning we associated to the features based ImagNet is hard, suggesting that the feature semantics have been repurposed to the very OOD nature of the dataset.
We now take a different approach. Instead of looking at images closest to specific feature, in Fig. 12 we look for features which are closest to messages deseribing images from given OOD dataset. The top feature presented in the figure is close to images from the CelebA dataset. The 10 closest ImageNet k images to that feature all have people's faces in the foreground, and generally represent people with swimsuits. In the second case, for the feature closest to images from the Places205 dataset, the ImageNet1k images closest that feature are all outdoor and urban settings, relevant to dataset about places. It therefore makes sense that those features were activated the most when communicating about given OOD image dataset, they capture information very relevant to those datasets.
P Insights from communication errors and examples of ImageNetlk/Place205 message similarity
In this section, we look at examples of communication game errors, since some qualitative sense of what messages expressing is also conveyed by the mistakes made by models, as shown in Fig. [3 Fig. 14 and Fig. 15 These are representative cases of when the VGG 11 sender ViT-B/16 receiver pair that trained in the population setup failed to converge on the target. In a large majority of them, the wrongly selected image is depicting an entily from the same class as the larget (first block of examples in the figures). The fact that often the relation is conceptual rather than perceptual (as in the second example of Fig. [3] where the target agents are communicatig about abstract categories, rather than low-level visual features. Of the minority of cases in which the wrongly selected image not from the same class, some errors are relatively transparent,
drawing of rocket, but the receiver picked a photograph of a rocket) confirms that
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Imagenet1K
CelebA
Places205
Imagenetlk
CelebA
Places205
Imagenet1k
CelebA
Places205
Figure 11: Three examples of SAE features and the images whose messages closest to the feature for every dalaset: Imagenet1k, CelebA and Places 205. The top feature, lines and 2, could be interpreted as featuring kitchen-related containers; the second feature, lines 3 and 4, involve airplanes; and the final feature, lines 5 and 6, involve transportation and large animals. Images from CelebA an Places205 are not seen at training time by neither sender nor SAE.
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Imagenetlk
CelebA
Places205
Imagenetlk
CelebA
Places705
Figure 12: Two examples of features close to images from OOD datasets. The top two lines illustrate a feature closest to CelebA images, and the bottom two lines are for a feature closest to Places205 images. In each case, the closest images to the feature from each dataset are shown. We obluscate faces of non- celebrities.
such as the ones the second block of the figures, where we see similar shapes and textures in the target and the wrongly picked distractor. Still, outside the frequent same-class errors, the most common case is the one illustrated by the last block of the figure, where the nature of the error not clear.
The examples in Fig. 16 instead, shed some further light how the agents generalize from the training dataset to out-of-distribution images. We show here just few examples, but they illustrate general trends we qualitatively observed in nunber of randomly selected cases. The top row of the figure shows target images that the receiver correctly retrieved in the Places205 dataset. For each of them, the bottom row shows the image in the ImageNet-basse message generated by the sender. The first example suggests that the message contains information about green and blue hues applied to natural textures. In the second example, communication probably focused the round shape and possibly the brown-ish background color. The third case suggests, like several of the examples above, that some messages refer to high-level semantic properties, as the two images contain very different-looking instances of the same high-level concept (a vehicle). Finally, we also encounter examples, such as the last one, where it is hard to interpret what the message is encoding.
communication training dataset that would be retrieved by the
Discretization and other channel perturbations
In the spirit of Carmeli et al. 2022 we wondered whether we could use an easy-to-optimize continuous channel at communication training time, but then discretize the messages at inference time, thus getting the "best of both worlds".
We communication-train trained the agents with dense continuous messages, but at evaluation time discretized their messages by rounding the value of each continuous dimension in the message to either 0 or 1. On the ImageNetlk test set, performance fell to near chance levels for all investigated thresholds, in both population and one-to-one settings. Performance loss was also drastic when transforming both the 16 and 64-dimensional continuous message into a one-hot vector, using the maximum value as the "hot" value.
The continuous messages thus seem to be genuinely dense. Indeed, further analysis showed that introducing Gaussian noise (e.g., P 0, 0.05) in the messages during evaluation impacted performance negatively
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Target Image Selected Distractor
Random Distractor
Same-class Errors
Transparent Errors
Opaque Errors
Figure 13: Examples from the ImageNet based test set of mistakes made by a VGG 11 sender + VTT- 1 receiver pair trained in the population sctup. We also show an additional random distractor from the batch for context.
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Target Image Selected Distractor
Random Distractor
Same-class Errors
Transparent
PODD0n
Opaque Errors
Figure 14: Examples from the ImageNet1k: based test set of mistakes made by a VGG 11 sender + VTT- 1 receiver pair trained the population sctup. We also show an additional random distractor from the batch for context.
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Target Image Selected Distractor
Random Distractor
Same-class Errors
Transparent Errors
Opaque Errors
Figure 15: Examples from the ImageNet 1k- based test set of mistakes made by a VGG 11 sender + VTT- 1 receiver pair trained the population setup. We also show an additional random distractor from the batch for context.
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Se octed Image
Clocest Training
Figure 16: Example images correctly selected in the Places205 experiment (top row) with the images that most closely match the corresponding messages in the Imagenetlk communication-tae trriiiinnttiiinggggg dataset (bottom row). Agents are VGG 11-ViT-B/16 pair.
en.0
an.3
003
am
an.a
042
dm.33
am31
36
am
-000
diute
0.5
am.l1
-6.44
dmuu
S8368
Prinpel Compaeett
Figure 17: Explained variance for PCA across messages (left) and correlation of the different continuous dimensions (right) from population setup. Messages were generated by senders exposed the ImageNet 1k test set.
(22% accuracy loss). Very small changes in the message representation significantly changed the image the receiver would choose.
That information is not sparsely encoded in the continuous vector dimensions, or in some lincar combinations of them, is confirmed by PCA analysis over 64-dimensional message space.A non-linear transformation is required to disentangle the space, performed with the SAE method in Section 4.4.2 The PCA eigenvalues are shown in Fig. 17 All PCA dimensions explain similar amounts of variance, with dimension holding significantly more information. The figure also shows that there is no significant correlation between the original continuous vector dimensions, suggesting that they all carry independent information.
39


Reviews:
[{"review_id": "QZqZRaOAQ7", "reviewer": "Reviewer_h4z3", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strength:\n1. The research topic has certain practical significance as it pertains to the communication issues of pre-trained visual networks in practical applications. It is related to the current development trends of artificial intelligence in fields such as autonomous driving and robotics, which can attract the attention of some researchers to this problem. \n\nWeakness:\n1. Lack of innovation in the method: The lightweight communication layer and self-supervised training method proposed in the paper are quite common in the field of deep neural network research. In essence, it aligns the feature spaces of different pre-trained models through structures such as linear layers or MLPs. Similar ideas have been put forward in some previous works [1, 2], and there are no unique technological innovation points.\n2. Insufficient depth and breadth of experiments: The experiments mainly focus on a limited number of network architectures and common datasets, lacking exploration of a wider range of network types and data from complex scenarios. For example, the differences between different models may be more significant in dealing with complex and changeable real-world scenarios. For instance, different models may handle different information modalities (such as vision, text, and tables). Moreover, not all models are neural network models; some may be non-neural network models (e.g., tree models are often used for tabular data). In real-world scenarios, different models may collect real-time streaming data feedback from the real world for incremental learning or online updates. Additionally, in practice, different agents should have different functions, and not all models should have the same functions as described in the paper. However, the paper does not conduct experiments to verify the proposed method's effectiveness in these complex scenarios. \n\n[1] Moayeri, Mazda, et al. \"Text-to-concept (and back) via cross-modal alignment.\" International Conference on Machine Learning. PMLR, 2023.\n[2] Liu, Haotian, et al. \"Visual instruction tuning.\" Advances in neural information processing systems 36 (2024).", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper focuses on the referential communication problem among different pre-trained visual deep networks. This problem is of certain research significance in application scenarios involving autonomous intelligent agents such as autonomous vehicles and robots. Through a series of experiments, the authors have confirmed that for pre-trained visual networks with different architectures, training objectives, and data, a referential communication protocol can be constructed via a lightweight communication layer and self-supervised training, which helps different networks transmit and interact with information. Moreover, this method has a certain generalization ability when dealing with finer-grained targets and unseen categories.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "1. Add supplement experiments in real-world scenarios mentioned in the \"Strengths And Weaknesses\"\n2. Add a discussion on the differences between the proposed method and previous methods", "broader_impact_concerns": "The paper does not discuss any of the broader impacts, such as ethical, social, or environmental aspects, that the research might have. In the current context where artificial intelligence technologies are widely used and highly concerned, research lacking a comprehensive consideration of such impacts is incomplete.\n\nFor example, if the research findings are applied in the field of autonomous driving, errors or deviations in the communication between networks could lead to serious safety accidents, triggering a crisis of trust in autonomous driving technology within society. In the application of robots, there may be issues related to the transmission and processing of private data. Without proper regulation and consideration, there will be potential risks of privacy leakage.\n\nIt is recommended that the authors conduct an in-depth analysis of these potential risks and add a dedicated section in the paper to elaborate on them in detail and discuss corresponding countermeasures so as to enhance the social responsibility and comprehensiveness of the research.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "SzNEwNtVvt", "reviewer": "Reviewer_Cm7a", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "**Strengths**\n1. While prior works consider the models with same architectures in this problem setup, this work further extends to models with different architectures.\n2. A comprehensive set of experiments considering different communication setups (one-to-one, homogeneous/heterogeneous, population) has been done.\n3. The problem itself is interesting and well-motivated.\n\n\n**Weaknesses**\n1. In Table 2, to more fairly compare homogeneous and heterogeneous setups, the amount of training data should be the same, rather than using 1/7 of the data for heterogeneous setup. However, this will lead to 7x more compute for all the experiments, so I would suggest a small scale experiment (i.e., only under 16-d messages and just one dataset) to justify the use of 1/7.\n2. The original image classification (or OOD generalization) for each dataset for each model is needed to better understand the effectiveness of the learned communication protocols. \n3. Since the pretrained models are highly likely to focus on high-level semantic information, the evaluation of OOD split (e.g., Table 3) or unseen datasets (e.g., Table 4) can be easy for the models. Therefore, I appreciate the analyses on single-class datasets and image perturbation datasets. (1) If possible, majority of the experiments should be done with image perturbation, otherwise it will be too simple for the models and the protocols. (2) To test the generalizability of the learned protocol, the work can learn the communication layers on data with no image augmentation and then test on batches with augmented images.\n4. For the image perturbation experiment, are there any speculations about why population setup experiences performance drop? The current population setup will put every visual model together and learn the communication layers simultaneously. What if we only add a new visual model (“learner”) to the learned community multiple times to reach a population community protocol? (This may also mitigate the “distinctive protocol within population setup” issue in Section 4.4.4).", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "No", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper investigates whether it is possible for a group of pretrained visual models to develop a communication protocol, though the visual models have different architectures and pretraining data. The paper focuses on the referential communication task, where light-weight adapters will be learned and a model (“sender”) will send encoded image representations (“message”) to another model (“receiver”) to select which image has the most similar representation to the message. The experimental results show that communication protocol can be learned in pairs of visual models, and even a group of visual models. The learned protocols can be generalized to out-of-distribution images. More analyses are done to study the properties of learned communication protocols.", "claims_and_evidence_yn": "No", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "**Typos**\n1. Page 9, the reference “Table 18” does not show the results of 64-dimensional communication.", "broader_impact_concerns": "N/A", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "9ilywryUP9", "reviewer": "Reviewer_edaQ", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "### Strengths\n1. The authors provide a solid motivation for the problem in the introduction.\n2. Extensive experiments covering various models, scenarios, and setups.\n3. Experiments are well-designed to evaluate the claims made by the authors.\n4. Results back up the claims that the authors made.\n5. The paper is generally well-written, except for the presentation of results in section 4.3.\n6. The authors are transparent about the experiments and results.\n7. Ablation studies such as probing for degenerate communication help gain insights about the learned protocol.\n\n### Weaknesses\n1. Although in this work the problem is not concerned with the alignment of different data types, the methodology used is very similar to those used in the multimodal learning community. The related work should discuss multimodal learning. There are similar works in multimodal learning including but not limited to the following:\n- Rohit Girdhar, Alaaeldin El-Nouby, Zhuang Liu, Mannat Singh, Kalyan Vasudev Alwala, Armand Joulin, and Ishan Misra. Imagebind: One embedding space to bind them all. In CVPR, 2023.\n- Haofei Yu, Zhengyang Qi, Lawrence Keunho Jang, Russ Salakhutdinov, Louis-Philippe Morency, and Paul Pu Liang. 2024. MMoE: Enhancing Multimodal Models with Mixtures of Multimodal Interaction Experts. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 10006–10030, Miami, Florida, USA. Association for Computational Linguistics.\n- Kasra Darvish, Edward Raff, Francis Ferraro, and Cynthia Matuszek. 2023. Multimodal language learning for object retrieval in low data regimes and in the face of missing modalities. Transactions on Machine Learning Research.\n- Chancharik Mitra, Abrar Anwar, Rodolfo Corona, Dan Klein, Trevor Darrell, and Jesse Thomason. 2024. Which One? Leveraging Context Between Objects and Multiple Views for Language Grounding. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 3177–3189, Mexico City, Mexico. Association for Computational Linguistics.\n- Paul Pu Liang, Yiwei Lyu, Xiang Fan, Jeffrey Tsaw, Yudong Liu, Shentong Mo, Dani Yogatama, Louis-Philippe Morency, and Russ Salakhutdinov. 2023. High-modality multimodal transformer: Quantifying modality & interaction heterogeneity for high-modality representation learning. Transactions on Machine Learning Research.\n- Gyan Tatiya, Jonathan Francis, Ho-Hsiang Wu, Yonatan Bisk, and Jivko Sinapov. Mosaic: Learning unified multi-sensory object property representations for robot learning via interactive perception. In 2024 IEEE International Conference on Robotics and Automation (ICRA), pp. 15381–15387, 2024. doi: 10.1109/ICRA57147.2024.10609998.\n- Valerii Likhosherstov, Anurag Arnab, Krzysztof Marcin Choromanski, Mario Lucic, Yi Tay, and Mostafa Dehghani. 2023. PolyViT: Co-training Vision Transformers on Images, Videos and Audio. Transactions on Machine Learning Research.\n2. The discussion of results (especially on page 9) reads more like a report of what happened than digestible insights and takeaways for the readers. I appreciate the transparency and details, but they should be used as evidence to deliver solid takeaways.\n3. What happens in a scenario where each heterogeneous model takes the same image and predicts the label independently (ablating the communication module), and then an agreement score is computed? Agreement score would be the number of times two models predict the same label, divided by all the test cases (similar to accuracy). This baseline provides intuition into whether the message-passing system between the models is more powerful than the case where each model predicts individually when it comes to the proposed application of communication between different models. In other words, I’m seeking an answer to the following question: if models can predict the same labels and have high agreement scores without aligning them (which eliminates the computational load of training the communication module), what would be the benefits of the proposed method for referential communication?\n4. Can you comment on why the accuracy of Heterogeneous is higher than Homogeneous when the dimension is 64 in Table 2? That seems counterintuitive.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "The authors study the problem of aligning multiple pre-trained vision models that are different from each other in terms of network architecture, training data, or other training setups. They refer to this problem as referential communication.\nThey take a self-supervised approach to perform this alignment so that models can refer to a specific object among a set of objects. The models that are aligned are called \"*community*,\" meaning that a communication protocol among them is learned. This work seeks to answer questions such as \"How can such a protocol be learned?\" and \"If we train such a protocol, can it be used for the subsequent new models outside the community?\"\n\nThe authors describe the setup as a \"*referential communication*\" game between a \"*sender*\" and a \"*receiver*.\" Both sender and receiver are structured by stacking light trainable feed-forward layers (which authors call \"*communication module*\") on top of a frozen vision backbone. Essentially, this communication layer is used to project the varied-length representations obtained by different vision models into a space where all vectors have the same dimension so that they can be compared.\nThe vision model in the sender processes an image to obtain its representation which then goes through the communication module to map it to the desired space. The sender then sends the final vector to the receiver. The receiver takes the target image, its representation from the sender, and other image candidates. The receiver then obtains the representations of all images using its neural network (which can be different from the sender's) and then passes all of them through the communication module. \n\nFinally, using cosine similarity, all the candidate representations are compared with the representation sent by the sender, and the most similar one is selected as the answer. The system is trained using cross-entropy loss between the receiver's predicted answer and the ground truth.\nWhen there are more than two neural networks, the sender and receiver are randomly sampled at each step.\nThis game can be seen as an instance of image retrieval, and the process is similar to contrastive learning.\n\nThe paper is in better shape than the previous submission, and the authors have addressed some of the requested changes from the previous submission such as the following:\n- Incorporation of diverse datasets\n- Additional insights about the communication module\n- Discussion about image retrieval\n\nThe contributions of the paper include the following.\nThe results show that the communication module can align heterogeneous models, learn high-level semantics, and generalize to novel objects (to some extent). Also, when trained, the protocol (a community of frozen pre-trained vision models with their trained communication module) can be used to incorporate new pre-trained models by only training the communication module of the new model instead of training all communication modules from scratch.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "In addition to the list of weaknesses above, here are some other changes that can enhance the paper.\n1. A list of contributions at the end of the introduction or in the conclusion would enhance the strength of the paper.\n2. In multiple cases, the authors use the word \"to some extent\" when discussing the results. Although I appreciate the transparency and honesty, it makes the reader question the strength of the evidence. An alternative could be reporting the numbers, or specifically mentioning in which case the performance is better and in which case it is worse.\n3. The structure of Table 3 makes it hard to read. At the bottom of page 8, you compare “Single-class (16)” against “Single-class (64),” but in the table, the two columns are not next to each other. I would suggest making two tables, one for different dimensions of “Single-class” and the other for different dimensions of OOD. Alternatively, if you want to compare single-class, OOD, and the original case against each other, you can have two tables where in one of them all the dimensions are 16, and in the other, all of them are 64. It makes it easier to read and compare numbers.\n4. On page 9 in the main body of the paper, you have a paragraph discussing tables and figures in the appendix. It is generally good practice to discuss figures/tables near where they are located. In this case, the description can go to the appendix, or the figures/tables can go into the main body of the paper. The current format is hard to follow. \n5. At the bottom of page 10, you mention “Receivers learn better and faster than senders.” Could you please describe why that is the case?\n6. It would be helpful to add one sentence describing what section 4.4.3 (Perturbation analysis) is proving. Sections 4.4.1 and 4.4.2 make sense as they provide insights into the learned alignment protocol. In other words, what does section 4.4.3 prove given that perturbing images reduces the accuracy?\n7. In the second paragraph of page 9, where you discuss the OOD results, you mention “there is a significant accuracy drop,” but you do not mention compared to which case.\n8. The resolution of Figure 1 should be enhanced. Also, there are a lot of unnecessary whitespaces in Figures 1, 4, and 5.\n9. Inconsistent caption placement. Captions of tables are on the top, while captions of figures are on the bottom.", "broader_impact_concerns": "To the best of my knowledge, there are no concerns or implications regarding the technical aspect of the paper other than the inherent biases in datasets and the lack of interpretability of neural networks. None of these are specific to this paper or a result of their work. \n\nRegarding the content, the following sentence on page 12 can be interpreted as an offensive sentence: \"For the more strongly OOD CelebA dataset, bottom left, pictures of celebrities tend to show either dark backgrounds, celebrities with dark skins, or in one case, makeup depicting an animal.\"\nI would recommend using another example if you have one or removing the last part \"makeup depicting an animal\" if you do not have another example.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: ## Summary of Paper
This paper explores if pretrained visual models with different architectures and pretraining data can develop a communication protocol in the referential communication task. The authors use a self-supervised approach to train the lightweight communication protocol, which helps different networks transmit and interact information. The experimental results show that communication protocol can be learned in pairs of visual models. Besides, the learned protocols can also be generalized to out-of-distribution images. The authors also study the properties of the emergent protocol.


## Advantages
1. Sufficient Experiments: This paper's experiments cover various models, scenarios, and different settings. These experimental results serve as accurate, convincing, and clear evidence to support the claims of this paper.
2. Interesting and Meaningful Research Question: The motivation of this paper is clear. And the research question is related to the development trends of artificial intelligence in fields such as autonomous driving and robotics, which can attract some individuals in TMLR's audience to be interested in the findings of this paper.


## Summary of Discussion
Reviewers generally appreciate the interestingness and practical significance of the research question. However, reviewers raised concerns regarding the lack of discussion on similar previous works (reviewer h4z3, edaQ), the absence of some additional experimental results (all reviewers), and the insufficient depth of analysis of experimental results (reviewer Cm7a, edaQ). After the rebuttal, most reviewers indicated that their concerns had been addressed.
Considering the advantages of this paper and the fact that the author successfully addressed some reviewers' concerns during the rebuttal stage, the editor tends to accept this paper with minor revision. The author needs to improve the paper according to the reviewers' comments to make the claims of this paper more convincing.
Decision: Accept (poster)