To Transfer or Not to Transfer: Suppressing Concepts from Source Representations
Authors: Vijay Sadashivaiah
Keerthiram Murugesan
Ronny Luss
Pin-Yu Chen
Chris Sims
James Hendler
Amit Dhurandhar
Venue: Accepted by TMLR ()

Abstract: With the proliferation of large pre-trained models in various domains, transfer learning has gained prominence where intermediate representations from these models can be leveraged to train better (target) task-specific models, with possibly limited labeled data. Although transfer learning can be beneficial in many applications, it can transfer undesirable information to target tasks that may severely curtail its performance in the target domain or raise ethical concerns related to privacy and/or fairness. In this paper, we propose a novel approach for suppressing the transfer of user-determined semantic concepts (viz. color, glasses, etc.) in intermediate source representations to target tasks without retraining the source model which can otherwise be expensive or even infeasible. Notably, we tackle a bigger challenge in the input data as a given intermediate source representation is biased towards the source task, thus possibly further entangling the desired concepts. We evaluate our approach qualitatively and quantitatively in the visual domain showcasing its efficacy for classification and generative source models. Finally, we provide a concept selection approach that automatically suppresses the undesirable concepts.

James Hendler Published in Transactions on Machine Learning Research (01/2024)
To Transfer or Not to Transfer: Suppressing Concepts from Source Representations
Vijay Sadashivaiah Deportment of Computer Science, Rensselaer Polytechnic Institute
sadasu2@rpi.ed.
Keerthiram Murugesan
keerthiram http://rmm..mmaggeea@i.mmmmmmmm
IBM Research, Yorktown Heights
Ronny Luss
rluss@us.
IBM Research, Yorktown Heights
Pin-Yo Yu Chen
pin- pin-gu.chen&ibm.com
IBM Research, Yorktown Heights
Chris Sims
simscSarpi.cm edu
Department of Cognitive Science, Rensselaer Polytechnic Institute
hendler@cs.rge wwt.rpc..ppp...o... edu
Department of Computer Science, Rensselaer Polytechnic Institute
Amit Dhurandhar
adharan Gus. www.imm..m.mmmmmm...mm.mm.m com
IBM Research, Yorktown Heights
Reviewed OpenReview: https: // openreview. net/foruue id= BNoDDED
Abstract
With the proliferation of large pre-trained models in various domains, transfer learning has gained prominence where intermediate representations from these models can be leveraged to train better (target) task-specific models, with possibly limited labeled data. Although transfer learning can be beneficial many applications, can transfer undesirable informa- tion target tasks that may severely curtail its performance in the target domain raise ethical concerns related to privacy and/or fairness. In this paper, propose novel ap- proach for suppressing the transfer of user-determined semantic concepts (viz. color, glasses, etc.) in intermediate source representations to target tasks without retraining the source model which can otherwise be expensive or even infeasible. Notably, we tackle a bigger challenge the input data as a given intermediate source representation biased towards the source task, thus possibly further entangling the desired concepts. We evaluate our approach qualitatively and quantitatively in the visual domain showcasing its efficacy for classification and generative source models. Finally, we provide concept selection approach that automatically suppresses the undesirable concepts.
1 Introduction
Deep neural networks (DNN) have achieved unprecedented performance in various computer vision and
natural language (NLP) problems such as image classification (Sun et al., 2017; Mahajan et. al., 2018), object detection (Girshick, 2015; Ren et al., 2015), segmentation (Long et al., 2015; He et al., 2017), question answering (Min al., 2017; Chung al., 2017), and machine translation (Zoph et al., 2016; Wang et al., 2018) etc. One of their strengths is learning task-specific hidden representations rather than relying on predefined image features. In ideal scenario, there abundant. labeled training samples to learn good

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Source Network
Classifier
conv1
conv2
conv3
encoder
2
44
digit
rotation
residue
2
Concept
Disentangling
Network E.g. IIN
2
rotation Prototype, Pr
sample images rotation
2
Pdigit
Pr
residue
rotation 90
25
CCT-s/CCT-cat
Target Network
conv1
encoder
Classifier
Figure 1: Illustration of our proposed approach rotated-MNIST dataset. We show how the rolation concept is suppressed from image before transferring to the target task. First, hidden representation =" from a pre-trained source network is factorized using concept disentangling network (CDN) into & E K {digit, rotation, residue), To suppress the rotation concept, the factor for sfotation set to a rotation prototype, Pr generated using few sample images with rotation. Now the CDN is used invert the modified factors to 3 which can either be directly used Controllable Concept Transfer-source (CCT-s) or concatenated with the target representation z" Controllable Concept "hansfer-comcatenaat (CC'I-cat)) to train the target task.
hidden representation. However, collecting sufficient training data is often expensive, time-consuming, unrealistic. In such scenarios, transfer learning (Pan & Yang, 2009) has emerged one of the promising learning paradigms. Transfer learning utilizes knowledge from information- rich source tasks to learn specific (often information-poor) target task.
One of the most widely used approaches for transfer learning is Sine-luming (Sharil Razavian et al., 2014) where the target DNN being trained is initialized with the weights of a source DNN that has been pre- trained large dataset from related task. Another popular approach involves combining (matching) the hidden representation (output gradient) of the target model with that of the source model (Jang et al., 2019; Li et al., 2018; Murugesan et al., 2022). These approaches are extensively used in improving prediction performance and robustness of many vision and NLP tasks (Hendrycks et al., 2019; Devlin et al., 2018) while reducing training time and resources. This effectiveness is partially attributed to the capability of DNNs to repurpose features and concepts for new tasks, as discussed by Neyshabur al. (2020).
In the realm of image classification, for example, visual concepts such shapes, colors, or textures learned while distinguishing between cats and dogs might be transferred to differentiate between tigers and wolves in new task. While concept transfer undosirable concepts encoded in source models to downstream tasks. For example, source model, trained to classify cats vs dogs, with most cal images in gray-scale and dog images in color, could incorrectly associate the concept of color to the images of the dog and pass this biased knowledge to downstream tasks. real-world applications, this could have scrious consequences. Among several examples, Steed Caliskan (2021) showed that embeddings extracted from pre-trained image models exhibit racial and gender bias that they learn from training datasets.
enhance the efficiency of models on related tasks, it could propagate
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While there are several approaches to mitigate the impact of unintended knowledge transfer in target models, ranging from data augmentation that balances target datasets (Park et al., 2018; Dixon et. al., 2018) to adversarial training for generating robust hidden representations against certain spurious concepts (Zhang et al., 2018; Wang et al.; 2020; Fan et al., 2021), suppressing undesirable information in the intermediate representation of the source model has been largely unexplored. Typically, large pre-trained (source) models are Icarned with imbalanced/ aabiase /biased data, and retraining these models to remove undesirable concepts might not be ideal. For one, it is expensive to retrain the source model, and access the data used in source model pretraining could be limited. Additionally, one can suppress the visual concepts in the imput data (i.e., masking pixels in input image), but latent concepts such as color, rotation, smiling, etc cannot be blocked without affecting other concepts. Our work takes a novel concept-based knowledge transfer approach where we address the following question:
How can we most effectively control the intermediate representation of a source model by suppress- ing a specific concepl while keeping other concepts (largely) intact before transferring to dounstream tasks?
Toward this goal, we propose transfer learning method, Controllable Concept Transfer (CCT), to sup- press the undesirable concepts (visual and latent) in the hidden representation of the source model before transferring them to a downstream task.
We introduce a novel method that utilizes a Concept Disentangling Network (CDN) to disentangle the con- cepts within the intermediate source representation and selectively transmit or suppress them for the target task. To the best of our knowledge, this disentanglement process has not been previously explored in the context of transfer learning. Our approach for CDN adapts recent works in interpretable disentanglement of images such as Invertible Interpretable Network (IIN) (Esser et al., 2020), Concept Activation Vectors (Kim et al., 2018; Zhou et al., 2018; Chen et al., 2020). We propose two transfer learning settings and demon- strate our approach to image classification tasks using three real-world datasets. Our system successfully suppresses concepts from the source intermediate representation in both settings. In addition, we propose a mutual information-basee metric based on Belghazi et al. (2018) to quantify the measure of concept sup- pression. Figure 1 illustrates our approach to suppress the concept of rotation from the source model using CDN explained later. We evaluate the performance of controllable concept transfer both qualitatively and quantitatively, Our qualitative analysis presents decoded images with different concepts suppressed.
2 Related Work
Transfer Learning from large pre-trained source model is a well-known approach to learning target tasks with limited labels (Pan & Yang. 2009). One of the most common transfer learning techniques is finetuning a pre-trained source model (Sharif Razavian et al., 2014), where network layers from the source model are frozen, and a new classifier head is trained for the target task. Recent works align the source and target features to transfer relevant knowledge either by matching network weights (Xuhong et al., 2018; Jang et al., 2019), attention maps (Li et al., 2018; Zagoruyko & Komodakis, 2016), Jacobians (Srinivas & Fleuret, 2018) or model reprogramming (Chen, 2022). Another line of work uses the source model to better guide the target network by transferring feature maps automatically to improve the target task performance (Murugesan et al., 2022). In all the above methods, the transferred knowledge is typically not interpretable. To understand what knowledge is being transferred from source and target networks, a few methods use attention maps to visualize the key features from the source model useful for the target task (Murugesan et al., 2022; Jang et al., 2019) or series of experimental analyses on the finetuned target model to study the importance of transferred knowledge (Neyshabur et al., 2020). In this paper, take different approach to transfer learning and propose a principled way of controlling what semantically meaningful concepts can be transferred from the source model to the target task.
With an increasing interest in Model Interpretabilityy, several approaches have been proposed to under- stand the inner workings of deep neural classifiers, specifically through human understandable high-level concepts as activation vectors (Kim et al., 2018; Zhou et al., 2018; Chen et al., 2020), or individual neurons (Erhan et al., 2009; Olah et al., 2017; Zeiler & Fergus, 2014; Bau et al., 2017). However, the representation
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of the semantic concepts is distributed across the hidden layers of the network (Fong & Vedaldi, 2018), and none of these methods can (confidently) claim that the features (i.e., neurons) identified from intermediate representations are associated only with the specific concept and are largely independent of other concepts (Montavon et al., 2017; Yosinski et al., 2015). A related line of work trains the models that explicitly en- code concepts in their intermediate representations (Koh et al., 2020; Chen et al.; 2020; Losch et al., 2019). However, this approach alters the network architecture and typically deteriorates overall performance (Zhou et al., 2016). Unlike these works, we propose a novel method transfer learning by blocking or allowing the relevant concepts from the source model to the target model for better interpretability.
Unlike DNNs, Generative Models are trained to produce images from samples of specific distribution. Variational auto-encoders (Kumar et al., 2018; Higgins et al., 2017) reconstruct images from a represen- tation whose marginal distribution is matched to a standard normal distribution. Generative Adversarial Networks (GAN) (Goodfellow et al., 2020; Hoang et al., 2018) map samples from a standard normal dis- tribution to realistic images as judged by a discriminator. While these approaches are invertible, they are not interpretable, are limited to representations with linear structure, and cannot. be applied to arbitrary representations from a source network. This motivates our choice for adapting frameworks such as Invertible Neural Networks (Dinh et al., 2014; Jacobsen et al., 2018; Kingma & Dhariwal, 2018; Esser et al., 2020) for our setup as they can identify disentangled concepts for interpretability, invert them and map them back to relevant features in the intermediate representations of the source model. StyleGAN (Karras et al., 2020) is a generative model that generates images with different "styles'. However, this is inappropriate for our setup since latent directional vectors need to be known for concepts one wishes to manipulate. Although these are known for certain concepts in specific datasets (viz., age and eye ratio in the FFHQ dataset), they are not readily available for arbitrary ones that one might want to manipulate. Morcover, finding the latent representation for a given real image, which would be required for the StyleGAN, is still topic of active research (Li et al., 2022) as the reconstructions from these patients can be imperfect.
Most stato-of-tho-art disentangled reprosentation learning methods are based on the framework of vari- ational auto-encoders (VAE) (Kingma & Welling, 2013; Rezende et al., 2014). However, these works do not enforce any structure on the latent space and many regularization techniques have then been proposed (Kumar et al., 2018; Higgins et al., 2017; Eastwood & Williams, 2018). Among works that enforce structure are those that learn structural causal models (Shen et al., 2022; Huang et al., 2023; Zhang et al., 2023), but they are used primarily for discriminative tasks. Xiao et al. (2020) takes different approach to disentan- gling within contrastive learning literature by embedding different semantic concepts in latent spaces, where each space is invariant to one semantic concept. A key differentiator between Xiao et al. (2020) and our approach is that we propose to suppress desired semantic concepts while keeping other concepts (largely) intact. On the contrary, their approach can transfer embeddings of one/few/al concepts to a downstream task. This is limitation, considering that it is impossible to pre-define embedding space for all possible semantic concepts. Lastly, data augmentation (Mitrovic et al., 2021; von Kügelgen et al., 2021) can also be viewed as an approach to mitigating features but generally assumes knowledge of the data generating process.
3 Controllable Concept Transfer Method
It has been widely observed that machine learning models learn context-specific correlations in datasets. For example, a model trained to classify different indoor scenes would learn to associate the presence of a bed to the output class of "bedroom" " vs couch to the output class of "living-romm". However, spurious associations in the transferred knowledge could hinder the performance of target task. For instance, an accent chair could be exclusively associated with "living-room" and when the model encounters novel environment where it is present in "bedroom", the source representation could be biased to classify the input as "living- room". A transfer learning method to modify the source intermediate representation to suppress certain concepl would prove useful in such silunsi The canonical way to suppress a certain concept from hidden representation would be to retrain the model with new images that satisfy the desired constraint. However, predicting how the retraining affects other related concepts is hard. Our work aims to tackle this problem directly and modify the hidden representation of the source model in a targeted manner. For instance, in our earlier example, we would ideally want suppress the concept of accent chair without affecting
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other concepts in the hidden representation of the source model before transferring it to the downstream task. Next, we provide brief overview of the Concept Disentangling Network based on recent work on interpretable disentanglement of input representations by Esser et al. (2020), after which we detail our work. This paper uses the terms "intermediate representation" and "hidden representation" interchangeably.
3.1 Background: Disentangling semantic concepts in intermediate representation
Let f be the given neural network with L layers that maps the input image z E Rhxwxel through series of hidden layers to the final output [(x). Often, intermediate representation E(x) E R#xxxC2 at a hid- den layer does not convey any semantic meaning and the mapping from the intermediate representation to semantically meaningful representation is well-defined, whereas the inverse is not straightforward. This pa- per is interested in invertible representation learning that maps intermediate representation to semantically meaningful concepts and vice verse. Esser et al. (2020) developed such approach to factorize the hidden representation from a model into user-defined semantic concepts. Specifically, they map arbitrary repre- sentations into a space of interpretable representations - a non-linear mapping between the two domains. This mapping is invertible, i.e., any modification in the domain of semantic concepts concurrently alters the original representation. In this scenario, it takes the flattened version of E(x), denoted = E IR (where N H W W. C)2 and factorize it into Σ = ( E RN, where each of the K factors of Es E IRNx with Ex=o N₂- = N represents an interpretable concept that is normally distributed (i 1). Calling this transformation Lion I, we have - = [()).
To encode semantic representation into each factor Es, they constrain (i) each factor Ey to vary with exactly one interpretable concept and (ii) & to be invariant to all other variations. This is ensured through training pairs x²), which specify semantics through similarity, i.e,, image pairs with a semantic concept of accent chair. Each semantic concept, indexed by F E [J. has image pairs (x", 2²) p(x", FF)F) to the corresponding factor ăp. To capture the remaining variability that is not captured by the K concepts, a residual concept uo is introduced. This ensures that the original representation space reflects any change made to the factorized semantic concept εμ. Calling this transformation 1-1, we have (3) = 2. Intuitively, the goal is to have a bijective mapping so that modifications of the disentangled semantic factors correctly translate back to the original representation. Please refer to Algorithm 2 in Appendix and Esser al. (2020) for further details.
3.2 Concept suppression and transfer
In this section, we focus our attention on controlling the concept transfer by suppressing undesirable concepts in the hidden representation of the source classifier and transferring relevant concepts to the downstream task. Let's contimue running example of classifying "bedroom" vs "living-room to describe our approach. At the high level, the goal is to take the hidden representation at layer L. - 1 of the pre-trained model and suppress the accent-chair concept without affecting other concepts.
How to suppress a concept using prototype? Let us assume that we have a pre-trained source network fs that takes input images x from a target task and produces hidden representation at layer L-1, fi-z(a)= = 2", i.c., the layer before classifier head of. Let us define a few semantic concopts specific the target task as accent-chair, bed, and couch. We first train the CDN to take the hidden representation =a and factorize it according to concepts such that 2 (I(=")),, where k E {accent - chair, bed, couch). Training is done using pairs of images that contain a common concept, i.c., the same bed to map Eged or the same couch for 24 Soouch In addition, there is residue factor Eyesidue that encodes all other variations unaccounted by these concepts. Since the CDN imposes a one-to-one mapping from the original representation space (=*) to a factorized space (Z*), we can edit the factorized representation بs Epotaioon without affecting the other factors. Given the CDN I, suppose one wants to suppress the accent-chair concopt. We sample a few example images (ri,-..,nx which do not have acoent-chair and pass them through our pre-trained source network and CDN to obtain their accent-chair embedding and take the mean create a prototype embedding, which is indicative of absence of the accent-chair concept. One
Pohair 1 E (I.
Where w. e. are height, width and channel dimensions input image
Where H. W. C are height, width, and channel dimensions of intermediate representation
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can now substitute accent-chair latent embedding of any image using the generated prototype to suppress its presence.
How to transfer? Next, we proceed to training the target model fe that takes as input images z and maps to a hidden representation at layer L - 1, (z) = z', the layer before target task classifier head e. The input image is also passed through the source model to get the source intermediate representation, which is then fed CDN. The accent-chair concept is suppressed by replacing the corresponding factor 2 ecccent-chair with the prototype, Pchair- The updated hidden representation I (2*) is then transferred to the target classifier ez, We consider two variations of transferring knowledge from the source model to the target task,
1. Controllable Concept Transfer source (CCT-s) where we freeze the layers up to L - 11 of source network, attach a classifier head for target task ez and train the classifier head for the target task with updated source representation, c% (z*)).
2. Controllable Concept Transfer concatenate (CCT-cat) where we concatenate the updated source representation with that of the pro-trained target network before passing it through the target classifier, <()((1-)(E*) @ 21)) where represents concalenation operation.
The entire pipeline is presented in Figure 1 and Algorithm 1 as CCT-cat (CCT-s follows similarly but would remove the target network f and combination operation in Step 15). Note that this approach works for multiple concepts by generating each prototype, ps and editing the corresponding factorized representation o for concept The above descriptions are for when one knows which concepts are to be blocked (in which lines 18-20 of Algorithm should be ignored).
How to suppress undesirable concepts automatically? It may sometimes be the case that there are too many concepts for human to decide what concepts to transfer/suppress mamually. Some concepts may introduce noise to the target task, or one may desire to get insights into which concepts offer performance improvements over the larget model. We consider two additional extensions of our framework: 1) Control- lable Concept Transfer - source with concept search (CCT-s(cs) and 2) Controllable Concept Transfer concatenate with concept search (CCT-cat(cs)). Both involve a procedure that searches for the optimal subset of concepts (from predefined set) to suppress (sce Algorithm 3 in Appendix). In prac- tice, learn Gumbel-softtax distribution for each concept independently to decide whether the concept should be suppressed not (line 19 of Algorithm 1). We can also use one Gumbel-softmax for all combi- nations of the concepts, but it may be infcasible when we have a large number of concepts. While we use Gumbel-Softmax sampling, other differentiable methods could also be applied to sample from structured discrete space (e.g., stochastic softmax (Paulus et al., 2020)).
4 How well does Concept Suppression work?
Before we study concept suppression's influence downstream tasks, we explore how well the concepts are suppressed from source hidden representation. In particular, consider two sets of experiments. (i) In the quantitative experiments, we adopt mutual information (MJ) based metric based on Belghazi et al. (2018) to measure the MI between concept and hidden representation before and after suppressing a concept. In both cases, we edit. the model using a single prololype to suppress one concept. al a time. (ii) In the qualitative experiments, we use an autoencoder architecture as a source model, which facilitates visualization of concept suppression by decoding CDN-edited hidden representations to human-underssandaale images.
4.1 Mutual Information Experiment
This experiment aims to quantitatively assess whether information about undesired concepts is contained or "hiding' in transferred representations. We consider two approaches popular in the interpretable disen- tanglement of input representations: Invertible Interpretable Network (IIN) (Esser et al., 2020), Concept Activation Vectors (Kim al., 2018; Zhou et al., 2018; Chen et al., 2020). To formally evaluate the perfor- mance of concept suppression in this scenario, employ
cetpmaatioo-thhorele analysis of the transferred

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Algorithm Controllable Concept Transfer Concatenate with Concept Search method (CCT-cat(cs))
Inputs: Target training datasel Dr; Target classifier loss (-(:;; Combination operation + Seed weight
parameters: [o] Source pre-trained network f"; Target pre-trained network f"; Number of Epochs E; Layer L; C List of K Concepts; Number of Epochs M between Concept Searches.
[ TRAIN-CDN
3: C+C
4: for CEC do
Randomll sample n images (2, without concept c.
Pc -- (T(E()))
end for
Randomly shuffle Dr-
9: for epoch E [1 E] do
10: for batch E Dr do
11:
I I Dr(batch].
12:
8 (ff
13:
for c C do
14:
22 t=tpo Pe end for
15:
16:
Update Worlbacht We [batch - 1] Tabatch Vw, Cof (1-1(11) end for
17:
18: if epoch mod M = 0 then
19: C CONCEPT-SEARCHIC)
20: end if
21: end for
22: Output: Trained model of with last iterate of We
representations. Specifically, we measure the mutual information between a specific concept and the hidden representations. To compute mutual information between concepts and neural network representations, we adapt the mutual information based neural estimator (MINE) proposed by Belghazi et al. (2018), where the authors present way estimate mutual information between high dimensional random variables using a trainable neural network that they term a statistics network. In simple words, given two random variables C for concept and Z for hidden reprosentation,
le [C( Z) = sup Ep [Tg log(Ep, X p 1pzle")))
AE0
where the expectations are estimated using samples drawn from Paz and Pc X Pz while the objective is maximized by gradient ascent. Please refer to Belghazi et al. (2018) for more details.
In particular, we estimate the MI between the concept C' and original hidden representation 2², T(C, 2*), and compare with the MI between concept C and edited hidden representation ž*, I(C, 5*). We start with a heterogeneons setup where the source model is trained on the rotated-collord--ENNI et al., 2017), where each colored image is rotated by a random angle drawn from r E (90, 180, 270}. However, we probe the source model with the rotated-colored-MNIIS datasct for MI estimation. Here, MI measured for color, rolation, and digit for hidden representations before and after suppressing color and rolation concepts. To estimate MI, we the color RGB vector and angle of rotation concept random variables C. These results are presented in Figure 2(a). The MI for digit does not change much when suppressing the other two concepts however, the MI for color and rotation drop siguificantly when the respective concepts are suppressed.
dataset (Cohen
We proceed to conduct experiments on the CelobA (Liu et al., 2015) dataset, largo-scale dataset of celebrity faces with attributes. First, source model trained on a multi-label classification task identify four dif- ferent concepts (ontts&Smmnn (Smiling, Wearing Lipstick, Heavy Makeup, High. Cheekbones). Next, each concept
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(a)
CDN (IIN)
(b)
CDN (CAS)
block
1.5
1.5
1.0
1.0
block Colk_
block Rotation
0.5
0.5
0.0
0.0
digit
color
rotation
digit
color
rotation
CDN (IIN)
(c)
0.4
block
None
block Smiling
block. Wearing Lipstick
block Heavy Makaup
High Chockbones
Smiling
Wearing_Lipstnn
Shg__Cheebbones
(d)
CDN (CAS)
0.4
MI
0.2
0.0
Smiling
Wearing Lipstick
Heavy_Matd Makeup
Sigh_Cheebbones
concept
Figure 2: Experiments mutual information (MI) between concepts and intermediate representation. Each group of bars represents a concept and the color of the bars represents the suppressed concept. MI for rotated-colored- -NNSS dataset digit, color, and rotation concepts when color and rotation are suppressed using (a) CDN (IIN) method and (b) CDN (CAS) method. MI for CelebA dataset where source model is trained on a multi-label classification task identify different concepts (Smiiling, Weoring Lipstick, Heavy Makeup, High Checkbones). Each concept is then suppressed individually and MI is reported for all concepts using (c) CDN (IIN) method and (d) CDN (CAS) method.
is suppressed, and MI is measured for every concept. To estimate MI, we use the binary value of the pres- ence/al /ace/nceccee of each concept random variable. Results are presented in Figure 2(b). As demonstrated by the plots, the MI for the suppressed concept reduces (almost) to zero in most cases. These findings suggest that the selected concept is suppressed from the updated hidden representation without significantly affecting the other concepts. Additionally, our choice of suppressing concepts by editing the source's final layers is justified by looking Figure 8, where we see that more complex concepts appear only in the later layers of the source network.
4.2 Comparing against concept activation based suppression (CAS)
To test the need for disentanglement to suppress concepts in hidden representations (INN,, we experiment with concept activation-based suppression (CAS) at the source intermediate representation motivated by previous works (Kim al., 2018; Zhou et al., 2018; Chen et al., 2020). In this experiment, we train linear classifier based on logistic regression for each concept. The hidden representation from the source network is used input, and each model predicts if the concept is /absent We then identify the top neurons for each concept based on model coefficients and use them to create the prototypes, Pt, for cach concept. To suppress a concept i, we just set those candidate neurons to the corresponding Pi. We tested this approach in rotated-colored- - and CelebA by measuring MI values similar to previous experiments. Results are presented
Figure 2(b, d). As demonstrated, the MI for all concepts remains high even after using CAS to suppress them. This suggests that the information about the concept is still present after directly suppressing the hidden representation space. On the other hand, suppressing through the factorized
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(a)
(b)
5 0
9
5 504/9 9
network. (a) In the first column, present randomly drawn colored-MNIST images. We then proceed to suppress the concept color from these images and show them in the second row. (b) For CelebA dataset, consider randomly drawn samples with the Eyeglasses attribute and proceed to suppress them. Examples of other concepts being suppressed are in the appendix.
Figure 3: Visualization of concept suppression using an autoencoder
representation of IIN leads much less mutual information. Hence, we chose to use IIN-based CDN for the remaining experiments.
4.3 Qualitative experiments using autoencoder architecture
We replace the source model classifier with an autoencoder to visually evaluate concept suppression. Consider that f takes the imput image x Rhxwxe and encodes it into the hidden representation == f=) e RN, N = H W C. Decoder d' is then used map the hidden representation back to the original image space T = (z) E Rhxuxe. First, consider a simple dataset where we add the concept of color to the MNIST dataset (colored-MNIST) (LeCun et al., 1998). A source model is trained to reconstruct the input images, and CDN trained to disentangle the concepts of digit and color. We then edit the color factor of few randomiy drawn sample images with the protolype Pe color and present results Figure 3(a). As demonstrated Figure 3(a), our method can suppress the concept of color without affecting the digit concept.
Next, proceed to CelebA dataset. We train the source model to reconstruct CelebA images and then train the CDN to discntangle three concepts Eyeglasses, No_ Beard, and Smiling. We then edit these concepts using corresponding protolypes, Results for suppressing the Eyeglasses concept in a few randomly drawn sample images are presented Figure 3(b). Additional figures for suppressing the concept of Smiling and No Beard are presented in Figure 9. In Figure 3(b), we see that the concept of Eyeglasses is successfully suppressed from sample images, demonstrating that this approach can suppress complicated concepts.
5 Evaluation on Classification tasks
The analysis presented in the previous section demonstrates that concept blocking is feasible. In this section, we consider how concept suppression affects the performance of downstream target task. In particular, present two scenarios of image classification tasks: (i) Transfer from the rotatod-EMNISTT trained source
model to the rolated-MNIST classification (ask, and (ii) Transfer between CelebFaces altribute classifiers. For the former task, we use deep 6-layer convolutional neural network (CNN) for the source model and a shallow 3-layer CNN for the target model. For the CelebFaces task, we used a ResNet34 (He et al., 2016) pre-trained on ImageNet for the source model and ResNet18 for the target model. Additional details about the experimental setup and datasets can be found in Appendix A.3. For each experiment, we consider two variants of transfer: Controllable Concept Transfer source (CCT-s) and Controllable Concept Transfer concatenate (CCT-cat) and compare the performance against training the target model independently (TG).
5.1 Transfer between heterogeneous datasets
In this experiment, we study the effect suppressing the concept of rotation in a notatod-EMNISS trained source model to rotated-MNIST transfer task. First, we pre-train the source model with the rotated-
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EMNIST dataset, aiming to classify 26 English letters. Next, train the CDN to factorize the concepts of digit and rotation using layer 1 representation of the pre-trained source model by probing it with rotated- MNIST images as inputs. Finally, we train the target task of classifying ten digits without suppressing any concepts, termed CCT-s(noodit) and (CTecit((ooedit), and compare the performance to rotation suppressod transfer, termed CCT-s(editt and CCT-cat (edit..(edit). To force the largel model rely on the source model for the rotation concept, we vary the amount of rotated training samples in the target dataset from 1% to 75%. Top-1 accuracy for these experiments is presented in Table with maximum accuracy in bold for each column. Each experiment is repeated thrice, and the mean accuracy is presented. As expected, at lower percentages of rotated samples (S 10), we see that suppressing the concept of rotation boosts the performance of transfer with CCT-s(edit) and CCT-cat(edit) performing best. We postulate that, at lower percentages of rotated Training samples in the target dataset, the targel model relies on the source model for a good representation of the rotation concept, and suppressing this concept makes the target task casier. As the percentage of rotated samples the training data increases, the target model learns a better representation of the rolation concept and relies less the source model. This evidenced by TG and CCTTcatt(oodiit having comparable performance at higher percentage of rotated samples.
Table Mean accuracy (over three runs) for rotAted-EMNIST to Sraated-MNIST transfer task. Experiments are conducted by varying the proportion (%) of rotated samples f90, 180, 270} in the training dataset from 1% to 75%.
We compare the performance of three models: Target only (TG), CCT-s and CCT-cat. For CCT-s and CCT-cat we conduct experiments without suppressing any concept (uoedit) suppressing rotation (edit).
Fraction (%) of rotated images in target task dataset
Method
1
3
4
5
6
8
9
10
25
50
75
TG
0.52
0.58
0.62
0.65
0.68
0.70
0.71
0.73
0.71
0.75
0.84
0.88
0.90
(noedit)
0.57
0.59
0.61
0.62
0.63
0.64
0.65
0.66
0.67
0.68
0.75
0.80
0.81
(noedit))
0.61
0.65
0.68
0.70
0.72
0.73
0.75
0.76
0.77
0.78
0.86
0.90
0.91
(ed-s(
0.68
0.68
0.69
0.69
0.69
0.69
0.69
0.69
0.70
0.70
0.73
0.75
0.77
(CT-cat(edit)
0.64
0.68
0.71
0.73
0.74
0.76
0.76
0.77
0.78
0.79
0.85
0.89
0.90
5.2 Evaluation on CelebFaces Attributes
In this experiment, we consider a homogeneous sctup where the transfer done between two CelobA tasks. We train the source model to identify four different concepts, Smiling, Wearing Lipslick, Heavy Makeup, and High. Checkbones, based on multi-label classifier. We then train CDN to factorize these concepts using layer L - representation of the source network. Finally, train the target binary gender clas- sification task. Specifically, we train several models with/witd without suppressing concepts, CCT-s(noedii). (erT-cat(noedit) CCT-s(edit), and (Cd-cat(edit), and compare against an independently trained target model (TG).
Table 2: Mean accuracy (over three runs) for CelebA transfer task. Experiments are conducted by suppressing concepts Smiling, Setaring_Tipstieee Heavy_Makent High Cheekbomes one at a time and suppreesing both Wear- ing Lipstick, Heavy_ Makeup at once (Both Makeup). As before compare the performance of three models: TG. CCT-s, and CCT--cat. For CCT-s and CCT-cat we perform experiments with (edit) and without (noedit) concept suppression.
Suppressed concept
Wearing
Heavy Makeup
High Checkbones
Both Makeup
Method
Smiling
Lipstick
TG
0.8463
CCT-s (noedit)
0.9173
CCT-s (edit)
0.9096
0.8310
0.8493
0.9106
0.7393
CCT-cat (noedit)
0.9243
CCT-cat (odit)
0.9196
0.8746
0.8830
0.9190
0.8353
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1.00
model
0.75
TG
accuracy
CCT-s(noedit)
0.50
CCT-cat(noedii)
CCT-s(cs)
0.25
CCT-cat(cs)
0.00
10hh110_Cnncets 4_ 4 Concepts
experiment
Figure 4: Top-1 accuracy of concept selection methods versus noedit methods in CelebA experiment. Each bar group represents several attributes considered, i.e., four vs ten. Each color represents the model under consideration.
As seen in Table 2, just using the source model CCT-s(noedit) improves performance in comparison to the independently trained target model (TG). We notice that suppressing the concepts of Smiling and Seeh__hheebones did not yield a significant impact on CCT-s(edit) performance, suggesting that these concepts are less relevant to the target task. However, suppressing W Staring_Lipstinn and Makeup individually causes a drop in CCT-s(edit) performance. Next, we suppressed both concepts simultaneously and found that the performance dropped further. This suggests that the target classifier is relying on Lipstick and that combining source and target representations in CCT-cal(edit) improves the performance compared to CCT-s(edit) while suppressing each concept.
concopts such as W carting_ipstiic
Makeup for classifying Male vs Not Not_Male Male. We also see
Concept search based experiments. We next add concept search to the same experimental setup and consider additional concepts (up to 10) to account for situation where the user cannot easily select concepts to suppress themselves. Results are illustrated in Figure 4 using ten concepts (left) and four concepts (right). We see here that concept search can offer similar performance with insights to be gained from examining which concepts are suppressed (most often), which we present in Figure 10. We observe that in 4 concepts experiment, both Heavy Makeup and W Wearing Lipstick are suppressed less, and in 10 concepts experiment, Wearing__ipppt
Beard and Laawy_Makeup are top 3 least suppressed concepts.
6 Evaluation on co-occurring concepts
Vision datasets often contain concepts that appear together more often than others, and training models using such datasets lead to a phenomenon called co-ocewrence bias. For instance, "person" al always co- occurs in images with "ski" or "skateboard" (Singh et al., 2020), and hence, model trained to identify these objects would suffer in the absence of a "person" them. In this section, we study such scenario where the source dataset has co-occurrence bias and aim to suppress this bias from transferring
downstream task.
Specifically, it has been shown that object recognition models can spuriously rely on the image background instead of the objects themselves (Ribeiro et al., 2016). We study this phenomenon using the WaterBirds dataset (Sagawa et al,, 2019), where waterbirds (or landbirds) are placed against water (or land) more often in training set. In contrast, the test set has an equal number of waterbirds (or landbirds) placed against water (or land). In this experiment, we use an ImageNet pre-trained source model and train our CDN to factorize Background and BirdTypee We then evaluate our downstream waterbirds task with Background suppressed source representation and present results in Table 3. We see that suppressing the background information from source representation improves downstream task performance in both CCT-s (edit) and CCT-cat (edit) models, further validating our approach in co-occurring concept. tasks.
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Table 3: Mean accuracy (over three runs) for WaterBirds transfer task. Experiments are conducted by suppressing Background concept. As before we compare the performance of three models: TG, CCT-s, and CCT-cctt For CCT-s and CCT-cat we perform experiments with (edit) and without (noedit) concept suppression.
Accuracy
TG
0.6392
(noess)
0.7812
CCT (nteddi(
0.8459
(CT-s(edit)
0.7956
(ed-cal(edit)
0.8464
7 Conclusions
We have seen in this paper how one can effectively suppress certain semantic concepts from being transferred from a source model to a target model while allowing other concepts and information to be transferred. While we were (largely) successful in this endeavor, there may be situations where it is difficult to suppress a certain concept while allowing others to pass. This is because concepts can be (statistically or causally) correlated, so suppressing one will lead to (at least partially) suppressing the other. For instance, it. may be impossible to suppress the Wearing Lipstick concept while allowing Heavy Makeup to be transferred. In the future, it would be interesting to consider cases where the user determines which concepts to suppress, which pass specifically and to arrive at strategy that best satisfies these requiremenis. The strategy may also involve informing the user that the constraints are impossible to satisfy.
Additionally, we suppressed concepts by setting the corresponding latent vector in the IIN disentangled representation to mean/ mean/median values based on images lacking that concept and then inverting back to the source intermediate representation. However, there may be other ways to set these values, possibly taking inspiration from the explainable AI and fairness literature (Došilović et al., 2018; Mehrabi et al., 2021), where methods such as SHAP (Lundberg & Lee, 2017) and MACEM (Dhurandhar al., 2019), there are different ways to determine mbas/base values indicative of no information.
8 Limitations and Future Work
A drawback of our proposed approach is the need for concept attributes (or labels) required to train CDN. However, if the concepts are well-dofined, fewer examples could be needed to encode those concepts. When transferring from a pre-trained source model that provides a rich representation to improve the target task, ensuring that these representations are not biased towards a particular concept by suppressing it outweighs the cost of annotating. Additionally, image attribute recognition methods such as (Cheng al., 2018; Zhang et al., 2022) can be used to identify attributes in given image a pre-processing step, but that is out of the scope of this work. An interesting, albeit challenging, direction for future work is learning which concepts are most relevant for transferring to downstream task on the fly.
Most datasets in typical benchmarks used for transfer learning do not provide concept-level attribution, and annotating them is outside the scope of this work. This limited our choice of experimental datasets to those that come with annotated attributes, such as CelebFaces, WaterBirds, etc. We also considered datasets such MS-COCO, where each image is labelled with person, animal, object, etc., labels. However, we find that the concepts in the CelebFaces dataset are richer in latent concepts. For instance, concepts such are more complex, and suppressing them is nontrivial compared to attributes present in MS- COCO, where blocking the pixels of the person bounding box can be considered suppression of the person attribute. Although we demonstrated our work on image datasets, one can easily extend our approach to transfer learning sctups tabular and NLP tasks, where each feature can be considered as a concept. We demonstrate such transfer using a proof of concept experiment on tabular dataset in Appendix A.4. We leave additional exploration of this direction for future work.
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A Appendix
A.1 Datasets
For the bulk of our experiments in Sections 4 and 5 we use the MNIST (LeCun et al., 1998), EMNIST (Cohen et al., 2017) and CelebFaces Attributes (CelebA) (Liu et al., 2015) datasets. Few examples from each dataset and corresponding concepts are presented in Figure 5
MNIST. MNIST a handwritten digits classification dataset with digits. There are 60,000 training examples and 10,000 testing examples. It subset of a larger set from National Institute of Standards and Technology (NIST) where each image is normaloodl and centered in fixed-size image. We introduce concept of rolation to these images by rotating 75% (unless herwiss specified) of images by one of 4 possible values {90, 180, 270}. Concept of color is added to images by multiplying each image channel with corresponding [R,G,B] values drawn from a uniform distribution.
EMNIST. EMNIST is a set of handwritten characters derived from NIST Special Database 19. For our experiments we use the EMNIST Letters datasct, which 26 class classification of english letters. There are 88,800 training examples and 14,800 lesting examples. We introduce concept of rolation to these images by rotating 75% (unless otherwise specified) of images by one of 4 possible values {90, 180, 270}. Concept of color is added to images by multiplying each image channel with corresponding [R,G.B] values drawn from a uniform distribution.
CelebFaces Attributes. CelebA is a large-scale face attributes prediction dataset with more than 200,000 images. Each image has 40 different attribute annotations. There are 162,770 train- ing examples, 19962 test examples and 19867 validation examples. For our experiments, in Sec- tions 4 we concepts of (Fyeglasses, No Beard, Smiling) and for experiments in 5 use
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N
3
rotated-colored-MMISTT
W
K
X
rotated-collred-EMMNITT
CelebA Smiling
CelebA No Beard
CelebA Eyeglasses
CelebA Wearing
CelebA - High- Cheekbonee
CelebA Heeea-aaavayauuuuu
Figure 5: Example images for rotated-colored-MNNG Shaated-colrre-EEMMNISS and CelebFaces Attributes.
(Smiling, Wearing_Täpstick Heavy_Miii Makeup, High__Chhhheee Shhh__hhhebooss.... For concept search experiment in- volving 10 concepts, we chose {" ("mmliin"", " (eeevy_Makeewe "aackeup"""Werrriiiie "W cearing_Lipsticte "High_CCeehllleee Cheekbones", "Mouth Slighlly Open", "Wearing Necklace". "Oval Face", "Eyeglasses"; "No Beard", " "Bangs").
(CUB) dataset (Wah et al., 2011) with image backgrounds from the Places dataset (Zhou et al., 2017). Each bird is labelled as one of Y = waterbird, landbird and place it on one of A water background, land background, In the training set waterbirds (landbirds) more frequently (90%) appear against a water (land) background, however the test set thore are equal number of samples in each four possible scenarios (waterbird-wateer waterbird-land, landbird-water, handbird-landl
Birds Waterbirds dataset combines bird photographs from the Caltech-UGSD
A.2 Models
To describe the architecture used in these experiments, use the following notation:
Conv2d(cin, Cout, k, 8, p): A two dimensional convolution operation that takes Con input channels and produce Cout output channels. A square kernel of size & is used. is the stride and is the padding.
ConvT2d((mn Cout: hi, 8, p): A two dimensional transposed convolution operation that takes Cin input channels and produce Cout output channels. A square kernel of size is used. s is the stride and is the padding.
Linear(Cin, Cout): A linear layer that maps an input vector ii E [Rem to an output vector v2 Roout
Autoencoder Architecture
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The architecture for Autoencoder experiment presented in 4 is based on Lucie al. (2018). Similar to Fsser et al. (2020), replace the batch normalization (Toffe & Szegedy, 2015) by activation normalization (Kingma & Dhariwal, 2018). Details regarding the architecture can be found in Table 4.
Table 4: Architecture of autoencoder model. Input images have size of x w x and are quadratic in nature h = w
Encoder
Decoder
Conv2d(3, 64, 4, 2, 1), ActNorm, Leaky
Cony 512, h/16, 1, 0), ActNorm, Leaky
(61v 128, 4, 2, 1), ActNorm, TeakkyRel.U00..
Conv (512, 256, 4, 2, 1), ActNorm, LeakyRel.UU0.2. Conv (2d 128, 4, 2, 1), ActNorm, LeakyReLU
C1n8 256, 4, 2, 1), ActNorm, ReakyReLU(002)
(256 512, 4, 2, 1), ActNorm, (...)
Conv' Tod 1228, 64, 4, 2. 1), ActNorm, Loak
C512 z, 4/16, 1, 0)
Conv (2n 3, 4, 2, 1), Tanh
MNIST/EMNII experiments
We use two different architectures for this transfer learning setup. larger layer network for source model which is the same as the encoder block of auto-encoder presented in Table with a fully connected layer classifier attached at the end (see Table 5). A smaller layer network for target model (see Table 6).
Table 5: Architecture of source model
Encoder
Classification Head
Conv2d(3, 64, 4, 1), ActNorm, LeakyReLUU00.)
Linear(2* 22,, Relosses)
C6nv2d(64, 128, 4, 2, 1), ActNorm, LeakyReLU(OO.)
Conv2d(128, 256, 4, 2, 1), Act.ormm Leaky Re (0.2
(2nv2d(256, 512, 4, 2, 1), ActNorm, (0.))))LU(0.22)
Conv2d(512, x", h/16, 1, 0)
Table 6: Architecture of source model
Encoder
Classification Head
C33v2d(3, 64, 4, 2, 1), ActNorm, LeakyReI.U(0.2)) C6642d(64, =", h/16, 1, 0)
Linear( (z', Relasses)
CelebFaces and Waterbirds experiments
We use two different architectures for this transfer learning setup. A larger ResNet34 for source model with a fully connected layer as classifier attached at the end. A smaller ResNet18 network for target model with fully connected layer attached at the end.
Concept Distentangling Network
As described in Section 3.1, use the Invertible Neural Network proposed in Esser et al. (2020) Invertible Interpretable Network. In this implementation, the network consists of three invertible layers stacked one on to of another to create an invertible block: coupling blocks (Dinh et al., 2016), actnorm (Kingma & Dhariwal, 2018) blocks and shuffligg blocks. After the input representation =" is passed through several invertible blocks, the output split into K factors (z We refer readers to Esser et (2020) for further details.
A Algorithm for training CDN
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Algorithm 2 TRAIN-CDN: Train Concept Disentangling Network
Inputs: CDN training dataset Dj; CDN loss Cz(-); Seed weight parameters: Wy[0]; Source pre-trained network 8*; Number of Epochs E', Layer L, K List of Concepts C to factorize.
2: Randomly shuffle D..
3: for epoch E [1 E*] do
4: for batch E D, do
(fa:Tblc)) + Dy [y[batch] Where (Ia, are pairs of samples encoding concept c e C. (z))2z) < (f., (z,))
W1 [blbatch] <- [batch - 1 Vw,
CI((() 2f)(c).
end for end for 10: Output: Trained model / with last. iterate of Wr
Algorithm for Concept Search
Algorithm 3 CONCEPT-SEARCH: Search Concepts to Suppress
1: Inputs: List of K Concepts C Number of Epochs E"; Wes from previous epoch.
2: Initialize Gumbel-So, ftmax weights We randomly if not initialized, Vc E C.
3: for epoch E [1 F²] do
4: for batch E Dr do
C Guubel-Sofftmnn (Wes).
Dr Db(bacc)]
2a + (x)]
8:
for C C do
0- + Pe
10: end for
11: Wes [bsthatch] + Woa [batch - 1] Thttch Vw, Ca +=)))
12: end for
13: end for
14: Output: Selected Concepts Č.
Statistics Network
For mutual information based experiments in Scction 4, we adapt a statistics network proposed in Belghazi al. (2018). We use custom sequence of 4 layer network that takes intermediate representation Z and concept vector C to estimate the mutual information between them. Details about the architecture is presented in Table 7.
Table 7: Architecture of statistics network for mutual information estimation.
Statistics Network
ConcatLayer((,,C)
Linear [Z] + [C]. 100), ReLU()
Linear 100), ReLU()
Linear(100, 1), ReLU()
A.3 Experimental details
For our experimental analysis in the main paper, we set the number of epochs for training to E = 50 for all models. We train all models using a batch size of 25 and a learning rate of 10-4 for the Adam optimizer (Kingma Ba, 2014). All models were randomly initialized before training. While training source classifier, CDN, target classifier,
different samples to ensure that each model is trained with non-overlapping
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samples. For example, train the source network in CelebA experiments with the standard training dataset, CDN and target classifier with split of validation samples. Testing is done the standard test dataset. Statistics network is trained by querying pre-trained source model with standard validation dataset and testing on standard test dataset. For creating prototypes for concepts, we use 100 samples.
The models were trained in parallel with the specifications shown Table 8.
Resource
Setting
CPU
IBM Power 9 CPU @ 3.15GHz
Memory
512GB
GPUs
1 x NVIDIA Tesla V100 16 GB
Disk
1.2 TB
OS
RedHat8
Table 8: Resources used for training
A.4 Additional experiments
Choosing the operation for creating prototypes
In order to to create prototypes for each concept, first query the pro-trained source model and CDN with 100 images that don't have concept, and aggregate the corresponding concept factor in CDN output. To choose the right aggregation factors, we experimented with simple operations such as mean, median, mode and setting the factor to zero. In order to visualize how effective each aggregation is at suppressing a concept, we visualize a few images reconstructed by using auto-encoder based source network as detailed in Section 4.1. We present these results for colored-MNIST images with color concept suppressed in Figure 6 and CelebA images with Eyglasses concept suppressed in Figure We find the mean and median performs the best and chose mean for rest of the experiments presented in Sections 4 and 5.
3
5
2
1
H
input
5
9
2
1
3
4
P
3
7
(
3
;
P mode
50
9
2
3
P median
5
3
4
P mean
Figure Visualization of color concept suppression using prototypes created different aggregation operators colored-MNIST images. We find that medion and mean performs the best.
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input
P. mode
P = median
P mean
Figure 7: Visualization of Eyeglasses concept suppression using protolypes created by different aggregation operators in CelebA images. We find that median meon performs the best.
Concept suppression different layers the network
In this experiment, compute mutual information for each concept in rolated--oolord--MNNSTT images different layers of the source network. As demonstrated in Figure 8, observe that only concept of color is learned at layer 2, which is successfully suppressed. More complicated concepts such as digit and rotation are only learned in later layers. It is thus best to intervene (i.e. suppress) in the final layer (i.e. layer 6), where the model has a good representation of all concepts before transferring the representation to downstream tasks.
layer
layer 3
layer 4
2
2
digit
color
rotation
digit
color
rotation
digit
color
rotation
layer
layer 6
1.5
1.5
block
1.0
block_Color
MI
0.5
block, Rotation
0.0
digit
digit
concept
Figure 8: Experiment comparing concept suppressing using hidden representations from different layers for the rotated-cclored- dataset. As can be seen the color concept learned first followed by digit and rotation. It is thus best to intervene (L.e suppress) in the final layer (i.e. layer 6) where the model has learned the critical concepts such as digit which we want to transfer.
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Autoencoder based concept removal images
(b)
(c)
Oee
Du
BD
Figure 9: Visualization of concept suppression using an autoencoder as source network. For CelebA dataset, we consider randomly drawn samples with Beard & Smiling attributes and proceed to suppress them individually. These results are presented in (a) and (b) respectively
Distribution of suppressed concepts in concept search experiments
(a)
2.1
concept
(b)
CCT adeat
0.
0.0
044
Smiiing
Hoary Westin_Luustt
4gg Oherrbooss
Barph_skyyyy_coom comeapt
Whatin_
Exeiissee
Figure 10: Distribution of concept suppression in the concept search experiments (i.e., CCT-s(cs) and (C))) We find that the suppressed concepts were meaningful for the task of Male vs. not Male classification, i.e., concepts not related to the task such as Smiling, High Satth__hheeees,, and Oval Face were more heavily suppressed. The concept suppression rate (a higher value indicates more suppression) is plotted.
Experiment using tabular data
In this section, we discuss proof of concept experiment using tabular data. Specifically, we consider the CDC Diabetes Health Indicators Dataset (CDC, 2017) available on the UC Irvine Machine Learning Repository. The Diabctes Health Indicators Dataset contains healthcare statistics and lifestyle survey information about people along with their diagnosis of diabetes. This balanced dataset has 70692 samples and we sample 7 features that have high correlation with the diabetes variable.
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Table Test accuracy source and target task using CDC Diabetes Health Indicators dataset.
Model
Test Accuracy
Source
74.13 (+0.001)
Target (scratch)
60.70 (+0.031)
Target (fine-tune)
73.20 (#0.035)
We first divide the dataset into source and target tasks, where the source task has 69,692 samples leaving the target task with 1000 samples. For our source task, we train a simple 2 layer multi-layer perceptron (MLP) to diagnose diabetes. Next, train the target task using similar layer MLP to diagnose heart disease. Finally, we finetune the pre-trained source model for the target task. Results for this experiment are presented in Table 9. We see that finetuning improves the accuracy of target task in comparison to a target model trained from scratch.
Next, we proceed edit each independent features by sctting the feature values zero, random, mean and median values. We present these results in Figure 11. Each bar plot group represents the feature that is removed, whereas the color indicates which operation Additionally, plot the Target (scratch) and Target (finetune) results for reference along with their average value plotted as dotted line. We see that even after editing features, accuracy of the scratch model is improved on the larget lask while remaining lower than the accuracy of the finetuned model without editing. Notably, relevant features such as High Cholesterol, General Health, High Blood Pressure and Age have high impact on heart disease prediction.
used to edit the corresponding feature.
80
edit
scratch
accuracy
70
zero rand
60
mean
median
finetune
50
HighBP
BMI
HighChol
GenHlth edited feature
DiffWalk
Age
PhysHIth
Experimental results for feature suppression tabular dataset. Each group of bar plots corresponds to the feature that is edited. The color of each bar represents the operation used for editing. For reference, we add the accuracy of (scrat()) and (fnettn))) model along with the average accuracy plotted dolted line.
Figure 11:
24


Reviews:
[{"review_id": "w9l6ENQTVs", "reviewer": "Reviewer_cTKJ", "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**\n\n+ The paper is well written with nice figures. I can follow it in an easy way.\n+ How to alleviate transfering undesirable information to target tasks is very important to many down-stream applications, which have practical usages.\n+ The proposed method is simple. While I am not an expert in this area, this paper seems to be novel to the best of my knowledge. \n+ The authors detail their implementation architecture in the appendix, which is very helpful for reproducibility.\n+ With the current provided results, the empirical significance can be revealed.\n\n\n**Weakness**\n\nMy concerns mainly lie in the evaluation part:\n\n+ Although it might be unnecessary, I wonder why the authors do not evaluate on the ImageNet dataset, which is bigger and more challenging than the MNIST & CelebA.\n+ According to Table 1-3, the quantitative evaluations are performed under the classification tasks. It might be interesting to see comparison results on more challenging tasks like segmentation and generation.\n+ It seems that the authors do not compare with other transfer learning methods which rely on fine-tuning and hidden representation matching. Can the authors add some discussions for this? Additionally, the chosen baselines in Section 4.2 are a bit outdated, which were published before 2020.\n+ The authors exclusively utilize datasets featuring a limited classification number for their comparisons. What about the results when extending the analysis to encompass 100 categories?\n+ In some cases (e.g. Table 1), the results of CCT-cat (noedit) are quite similar to those of CCT-cat (edit). Plus that CCT-cat (noedit) constantly outperforms CCT-cat (edit) in Table 2, this makes me somewhat hesitate to accept the design generality of the CCT-cat framework.", "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 attempts to improve transfer learning by suppressing a specific concept in the intermediate representations of a source model while keeping other concepts (largely) intact. To this effect, two specific algorithms named as CCT-s and CCT-cat are proposed. Evaluations are performed on the classification task across different datasets to demonstrate the effectiveness of the developed framework.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "I just have some minor exposition issues to point out:\n\n+ It looks that the opening of Section 3 is a bit redundant as the contents are introduced in the **Introduction** Section in a detailed way. So I think the authors could simplify it with a shorter paragraph.\n+ I am not an expert in transfer learning. This submission proposes a new methodology to control the intermediate representation of a source model by suppressing a specific concept while keeping other concepts intact, which plays a core role. However, I am a bit confused by why controlling the intermediate representation is better than adjusting the input data. Could the authors append a bit more discussion in the **Introduction** part with some examples? I guess this can make this submission more self-contained?\n+ Compared to suppressing concepts in hidden representations (IIN), could the authors provide the classification results on rotated-MNIST dataset (for Table 1) and CelebA dataset (for Table 2) besides Figure 2. This can make the readers have a more comprehensive understanding of the effectiveness of the developed CCT-cat / CCT-s.", "broader_impact_concerns": "It would be better if the authors add some short discussions on the social impact side, although I do not see any significant impact concern.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "19fAEhbYTK", "reviewer": "Reviewer_BMpx", "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:\n- This paper proposes an interesting idea that can be an effective way to enhance OOD generalization\n- The experiments are quite extensive and effective. It is interesting to find that the MI of each concept is successfully suppressed compared to other concepts. \n\nWeaknesses:\n- The writing of this paper could be further polished. In my opinion, this paper is not expressed straightforwardly enough.\n- Some closely related works that also use the disentanglement technique are not properly referred to:\nMitrovic et al., Representation learning via invariant causal mechanisms, ICLR 2021\nKügelgen et al., Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style, NeurIPS 2021\nShen et al., Weakly supervised disentangled generative causal representation learning, JMLR 2022\nHuang et al., Harnessing Out-Of-Distribution Examples via Augmenting Content and Style, ICLR 2023\nZhang et al., Identifiability guarantees for causal disentanglement from soft interventions, arXiv 2023\n- The method is still questionable due to three aspects:\n\t1. How can you be sure that each feature dimension can be specifically assigned to a certain concept? Moreover, how to make sure that the concept features from the prototypes and extracted features are corresponding to each other? If there is no evidence to support the above two questions, this method could be untrustworthy in practice.\n\t2. This method requires examples to produce prototypes. However, it could be very hard to sample the ideal examples. The authors state that they sample randomly, but the presented examples are nearly identical to their corresponding images in Fig 3.\n\t3. There is no theoretical analysis to support this concept suppression technique.\n- Moreover, why the performance of CAS is much worse than IIN? Is there an intuitive explanation?", "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 studies the transfer of features in out-of-distribution generalization problems. Specifically, the existence of spurious correlation could largely hinder the learning performance on unknown target distributions. To address this issue, the authors propose to suppress some of the harmful features that do not exist in the target domain. Such methodology is instantiated by a concept disentanglement network that leverages the extracted feature of natural images that do not have spurious correlations. By replacing the features from the target domain data with the identified spurious feature that is free from the undesired concept, the newly produced feature can successfully have its concepts suppressed. Therefore, the transfer learning performance could be further improved. Extensive experiments have been conducted to evaluate the performance of this paper.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "Please follow the weaknesses as a suggestion to improve this paper.", "broader_impact_concerns": "The authors have discuss this in the paper, no further concerns.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "rYAdquNSoP", "reviewer": "Reviewer_4k7z", "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": "Below is the list of strengths and weaknesses of this paper, I also include two questions for the authors in the weakness part.\n\n **Strengths**\n* The paper is easy to follow.\n* The problem that the authors try to solve is \n    important.\n* The experimental results have verified clearly the main idea.\n\n**Weaknesses**\n* The contribution of this method is limited, your main contribution is just a trick that filters out unnecessary information by replacing it with the prototype before transfer to the target domain.\n* Your work may not be the first one in this direction. Under the contrastive learning setting, [1] has proposed a similar method, where their target also removes the factor that has a negative effect when transferred to downstream tasks. I think it could be a good baseline to make a comparison with your method.\n* As you have indicated, the main weakness of your method is just to remove the concept that is independent of other ones. In real applications, these concepts tend to have complex correlations with each other. And this unintentionally reduces the impact of this method.\n* I think because of the above weakness, your experiment has not shown this approach performance under common benchmarks for transfer learning like CIFAR, ImageNet dataset, etc, which should be considered when working with domain adaptation.\n* The writing needs to be polished more, especially the introduction and related works. Some minor problems that I have found: your citation format is not the same throughout the paper, and some of the concept names like the prototype (which is used to replace the undesirable factors), are not highlighted enough about their role.\n\nBesides, I have two questions related to your method:\n* How could you define a concept?\n* Could you provide the computation cost in order to train these concepts?\n\n**Reference**\n\n[1] Tete Xiao, Xiaolong Wang, Alexei A Efros, Trevor Darrell. \"What Should Not Be Contrastive in Contrastive Learning \n\", ICLR 2021, https://openreview.net/forum?id=CZ8Y3NzuVzO", "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 studies a method in order to improve the performance of transfer learning tasks. First, they analyze under some specific settings, a few learned features from the source model can hurt target model accuracy. Then, they propose a pipeline that can remove undesirable information from the source feature vector before transferring it to the target task. To filter out these negative factors, authors utilize an Invertible Interpretable Network (IIN) to disentangle the extracted vector from the source model into a list of concepts. After that, they suppress negative concepts by replacing them with corresponding prototypes (concepts that do not relate to the undesirable concepts). Finally, authors have verified how their approach can improve transfer learning tasks between source and target domains under different experiment settings.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "Please refer to the \"Weaknesses\" part of the review.", "broader_impact_concerns": "No concerns about the ethical implications have been identified.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: This paper investigates the removal of spurious features in transfer learning, with an emphasis on the latent space. Specifically, the nuisance concept is replaced by an *average* concept without considering the nuisance concept.

All the reviewers (including this AE) like the proposed concept and acknowledge the promise of the proposed approach. Simultaneously, several reviewers raised concerns about the paper. Below are the main concerns in the reviews and discussions.

> 1. [About the main contribution]  (This paper) utilized the existing architecture to disentangle intermediate features into the latent spaces with pre-defined concepts ….. Basically, … this contribution is not novel enough.
 
This AE agrees with the novelty evaluation. However, based on the TMLR acceptance criteria, novelty should not be considered a reason to reject the paper (it may be a factor in considering a featured paper).

Instead, TMLR is based on the following question – would some people in the TMLR audience be interested in the results of this paper? I think the answer is **YES** because all the reviewers think this paper is interesting.


> 2. [About the definition of concept] At this time, these concepts are defined manually or have been pre-defined before (like the CelebFaces dataset), which is hard to obtain and time-consuming in real-world datasets and these concepts could be changed along with tasks….

I think this is a major limitation of the proposed approach. However, in many cases, we have the concept, which is referred to as metadata. Furthermore, I think that the identification (or discovery) of concepts should be considered a new problem in transfer learning. It is very similar to causal discovery. Therefore, it is a bit unrealistic to achieve all objectives within one paper.

> 3.  [About the experiments] its experiment parts can be further improved on large-scale datasets like ImageNet.
 
I agree with the experimental limitation on high-dimensional datasets such as images. While I think this method has value, for example, considering transfer learning in a tabular or NLP dataset, this method should work because each feature is a concept of its own.


**Decision** 

Based on the reviews and my reading, I think this paper is acceptable with minor revisions.
- I would suggest that the authors include a section discussing the limitations or weaknesses (2-3) and possible future actions. 
- Regarding weakness (3), I would suggest a proof-of-concept experiment on tabular data. Specifically, it may not be necessary to consider representation learning, but only the operation on the concepts.
Decision: Accept (poster)