GenAI4ELab/papercli-papers huggingface dataset:
DeepGuiser: Learning to Disguise Neural Architectures for Impeding Adversarial Transfer Attacks
Authors: Yi Cai; Chenyu Wang; Xuefei Ning; Zixuan Zhou; Dimin Niu; Huazhong Yang; Yu Wang
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Security is becoming increasingly critical in deep learning applications. Recent researches demonstrate that NN models are vulnerable to adversarial attacks, which can mislead them with only small input perturbations. Moreover, adversaries who know the architecture of victim models can conduct more effective attacks. Unfortunately, the architectural knowledge can usually be stolen by the adversaries by exploiting the system-level hints through many side channels, which is referred to as the neural architecture extraction attack. Conventional countermeasures for neural architecture extraction can introduce large overhead, and different hardware platforms have diverse types of side-channel leakages such that many expert efforts are needed in developing hardware-specific countermeasures. In this paper, we propose DeepGuiser, an automatic, hardware-agnostic, and retrain-free neural architecture disguising method, to disguise the neural architectures to reduce the harm of neural architecture extraction attacks. In a nutshell, given a trained model, DeepGuiser outputs a deploy model that is functionally equivalent with the trained model but with a different (i.e., disguising) architecture. DeepGuiser can minimize the harm of the follow-up adversarial transfer attacks to the deploy model, even if the disguising architecture is completely stolen by the architecture extraction attack. Experiments demonstrate that DeepGuiser can effectively disguise diverse architectures and impede the adversarial transferability by 13.87% ∼ 32.59%, while only introducing 10% ∼ 40% extra inference latency.
Coordination Scheme Probing for Generalizable Multi-Agent Reinforcement Learning
Authors: Hao Ding; Chengxing Jia; Cong Guan; Feng Chen; Lei Yuan; Zongzhang Zhang; Yang Yu
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Coordinating with previously unknown teammates without joint learning is a crucial need for real-world multi-agent applications, such as human-AI interaction. An active research topic on this problem is ad hoc teamwork, which improves agents' coordination ability in zero-shot settings. However, previous works can only solve the problem of a single agent's coordination with different teams, which is not in line with arbitrary group-to-group coordination in complex multi-agent scenarios. Moreover, they commonly suffer from limited adaptation ability within an episode in a zero-shot setting. To address these problems, we introduce the Coordination Scheme Probing (CSP) approach that applies a disentangled scheme probing module to represent and classify the newly arrived teammates beforehand with limited pre-collected episodic data and makes multi-agent control accordingly. To achieve generalization, CSP learns a meta-policy with multiple sub-policies that follow distinguished coordination schemes in an end-to-end fashion and automatically reuses it to coordinate with unseen teammates. Empirically, we show that the proposed method achieves remarkable performance compared to existing ad hoc teamwork and policy generalization methods in various multi-agent cooperative scenarios.
Noise Transforms Feed-Forward Networks into Sparse Coding Networks
Authors: Trenton Bricken; Bruno Olshausen; Gabriel Kreiman
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: A hallmark of biological neural networks, which distinguishes them from their artificial counterparts, is the high degree of sparsity in their activations. Here, we show that by simply injecting symmetric, random, noise during training in reconstruction or classification tasks, artificial neural networks with ReLU activation functions eliminate this difference; the neurons converge to a sparse coding solution where only a small fraction are active for any input. The resulting network learns receptive fields like those of primary visual cortex and remains sparse even when noise is removed in later stages of learning.
MLPInit: Embarrassingly Simple GNN Training Acceleration with MLP Initialization
Authors: Xiaotian Han; Tong Zhao; Yozen Liu; Xia Hu; Neil Shah
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Training graph neural networks (GNNs) on large graphs is complex and extremely time consuming. This is attributed to overheads caused by sparse matrix multiplication, which are sidestepped when training multi-layer perceptrons (MLPs) with only node features. MLPs, by ignoring graph context, are simple and faster for graph data, however they usually sacrifice prediction accuracy, limiting their applications for graph data. We observe that for most message passing-based GNNs, we can trivially derive an analog MLP (we call this a PeerMLP) with an equivalent weight space, by setting the trainable parameters with the same shapes, making us curious about how do GNNs using weights from a fully trained PeerMLP perform? Surprisingly, we find that GNNs initialized with such weights significantly outperform their PeerMLPs, motivating us to use PeerMLP training as a precursor, initialization step to GNN training. To this end, we propose an embarrassingly simple, yet hugely effective initialization method for GNN training acceleration, called \mlpinit. Our extensive experiments on multiple large-scale graph datasets with diverse GNN architectures validate that MLPInit can accelerate the training of GNNs (up to 33× speedup on OGB-Products) and often improve prediction performance (e.g., up to $7.97\%$ improvement for GraphSAGE across $7$ datasets for node classification, and up to $17.81\%$ improvement across $4$ datasets for link prediction on metric Hits@10). The code is available at https://github.com/snap-research/MLPInit-for-GNNs.
Learning to Generate All Feasible Actions
Authors: Mirco Theile; Daniele Bernardini; Raphael Trumpp; Cristina Piazza; Marco Caccamo; Alberto Sangiovanni-Vincentelli
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Several machine learning (ML) applications are characterized by searching for an optimal solution to a complex task. The search space for this optimal solution is often very large, so large in fact that this optimal solution is often not computable. Part of the problem is that many candidate solutions found via ML are actually infeasible and have to be discarded. Restricting the search space to only the feasible solution candidates simplifies finding an optimal solution for the tasks. Further, the set of feasible solutions could be re-used in multiple problems characterized by different tasks. In particular, we observe that complex tasks can be decomposed into subtasks and corresponding skills. We propose to learn a reusable and transferable skill by training an actor to generate all feasible actions. The trained actor can then propose feasible actions, among which an optimal one can be chosen according to a specific task. The actor is trained by interpreting the feasibility of each action as a target distribution. The training procedure minimizes a divergence of the actor's output distribution to this target. We derive the general optimization target for arbitrary f-divergences using a combination of kernel density estimates, resampling, and importance sampling. We further utilize an auxiliary critic to reduce the interactions with the environment. A preliminary comparison to related strategies shows that our approach learns to visit all the modes in the feasible action space, demonstrating the framework's potential for generating multimodal action distributions.
CBP-QSNN: Spiking Neural Networks Quantized Using Constrained Backpropagation
Authors: Donghyung Yoo; Doo Seok Jeong
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Spiking Neural Networks (SNNs) support sparse event-based data processing at high power efficiency when implemented in event-based neuromorphic processors. However, the limited on-chip memory capacity of neuromorphic processors strictly delimits the depth and width of SNNs implemented. A direct solution is the use of quantized SNNs (QSNNs) in place of SNNs with FP32 weights. To this end, we propose a method to quantize the weights using constrained backpropagation (CBP) with the Lagrangian function (conventional loss function plus well-defined weight-constraint functions) as an objective function. This work utilizes CBP as a post-training algorithm for deep SNNs pre-trained using various state-of-the-art methods including direct training (TSSL-BP, STBP, and surrogate gradient) and DNN-to-SNN conversion (SNN-Calibration), validating CBP as a general framework for QSNNs. CBP-QSNNs highlight their high accuracy insomuch as the degradation of accuracy on CIFAR-10, DVS128 Gesture, and CIFAR10-DVS in the worst case is less than 1\%. Particularly, CBP-QSNNs for SNN-Calibration-pretrained SNNs on CIFAR-100 highlight an unexpected large increase in accuracy by 3.72\% while using small weight-memory (3.5\% of the FP32 case).
TransFool: An Adversarial Attack against Neural Machine Translation Models
Authors: Sahar Sadrizadeh; Pascal Frossard; Ljiljana Dolamic
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Deep neural networks have been shown to be vulnerable to small perturbations of their inputs known as adversarial attacks. In this paper, we consider the particular task of Neural Machine Translation (NMT), where security is often critical. We investigate the vulnerability of NMT models to adversarial attacks and propose a new attack algorithm called TransFool. It builds on a multi-term optimization problem and a gradient projection step to compute adversarial examples that fool NMT models. By integrating the embedding representation of a language model in the proposed attack, we generate fluent adversarial examples in the source language that maintain a high level of semantic similarity with the clean samples and render the attack largely undetectable. Experimental results demonstrate that, for multiple translation tasks and different NMT architectures, our white-box attack can severely degrade the translation quality for more than 60% of the sentences while the semantic similarity between the original sentence and the adversarial example stays very high. Moreover, we show that the proposed attack is transferable to unknown target models and can fool those quite easily. Finally, our method leads to improvement in terms of success rate, semantic similarity, and fluency compared to the existing attack strategies both in white-box and black-box settings. Hence, TransFool permits to better characterize the vulnerability of NMT systems and outlines the necessity to design strong defense mechanisms and more robust NMT systems for real-life applications.
Continuous-Discrete Convolution for Geometry-Sequence Modeling in Proteins
Authors: Hehe Fan; Zhangyang Wang; Yi Yang; Mohan Kankanhalli
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: The structure of proteins involves 3D geometry of amino acid coordinates and 1D sequence of peptide chains. The 3D structure exhibits irregularity because amino acids are distributed unevenly in Euclidean space and their coordinates are continuous variables. In contrast, the 1D structure is regular because amino acids are arranged uniformly in the chains and their sequential positions (orders) are discrete variables. Moreover, geometric coordinates and sequential orders are in two types of spaces and their units of length are incompatible. These inconsistencies make it challenging to capture the 3D and 1D structures while avoiding the impact of sequence and geometry modeling on each other. This paper proposes a Continuous-Discrete Convolution (CDConv) that uses irregular and regular approaches to model the geometry and sequence structures, respectively. Specifically, CDConv employs independent learnable weights for different regular sequential displacements but directly encodes geometric displacements due to their irregularity. In this way, CDConv significantly improves protein modeling by reducing the impact of geometric irregularity on sequence modeling. Extensive experiments on a range of tasks, including protein fold classification, enzyme reaction classification, gene ontology term prediction and enzyme commission number prediction, demonstrate the effectiveness of the proposed CDConv.
Understanding Zero-shot Adversarial Robustness for Large-Scale Models
Authors: Chengzhi Mao; Scott Geng; Junfeng Yang; Xin Wang; Carl Vondrick
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Pretrained large-scale vision-language models like CLIP have exhibited strong generalization over unseen tasks. Yet imperceptible adversarial perturbations can significantly reduce CLIP's performance on new tasks. In this work, we identify and explore the problem of adapting large-scale models for zero-shot adversarial robustness. We first identify two key factors during model adaption--training losses and adaptation methods--that affect the model's zero-shot adversarial robustness. We then propose a text-guided contrastive adversarial training loss, which aligns the text embeddings and the adversarial visual features with contrastive learning on a small set of training data. We apply this training loss to two adaption methods, model finetuning and visual prompt tuning. We find that visual prompt tuning is more effective in the absence of texts, while finetuning wins in the existence of text guidance. Overall, our approach significantly improves the zero-shot adversarial robustness over CLIP, seeing an average improvement of 31 points over ImageNet and 15 zero-shot datasets. We hope this work can shed light on understanding the zero-shot adversarial robustness of large-scale models.
The Surprising Effectiveness of Equivariant Models in Domains with Latent Symmetry
Authors: Dian Wang; Jung Yeon Park; Neel Sortur; Lawson L.S. Wong; Robin Walters; Robert Platt
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: Extensive work has demonstrated that equivariant neural networks can significantly improve sample efficiency and generalization by enforcing an inductive bias in the network architecture. These applications typically assume that the domain symmetry is fully described by explicit transformations of the model inputs and outputs. However, many real-life applications contain only latent or partial symmetries which cannot be easily described by simple transformations of the input. In these cases, it is necessary to learn symmetry in the environment instead of imposing it mathematically on the network architecture. We discover, surprisingly, that imposing equivariance constraints that do not exactly match the domain symmetry is very helpful in learning the true symmetry in the environment. We differentiate between extrinsic and incorrect symmetry constraints and show that while imposing incorrect symmetry can impede the model's performance, imposing extrinsic symmetry can actually improve performance. We demonstrate that an equivariant model can significantly outperform non-equivariant methods on domains with latent symmetries both in supervised learning and in reinforcement learning for robotic manipulation and control problems.
A MULTI-SCALE STRUCTURE-PRESERVING HETEROLOGOUS IMAGE TRANSFORMATION ALGORITHM BASED ON CONDITIONAL ADVERSARIAL NETWORK LEARNING
Authors: Rui Xiang; Guo-yo Wang; Pan Yu
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Image transformation model learning is a basic technology for image enhancement, image super-resolution, image generation, multimodal image fusion, etc. which uses deep convolutional networks as a representation model for arbitrary functions, and uses fitting optimization with paired image training sets to solve the transformation model between images in the different sets. Affected by the complex and diverse changes of the 3D shape of the actual scene and the pixel-level optical properties of materials, the solution of the heterologous image conversion model is an ill-posed problem. In recent years, most of the proposed conditional adversarial learning methods for image transformation networks only consider the overall consistency loss constraint of the image, and the generated images often contain some pseudo-features or local structural deformations. In order to solve this problem, using the idea of multi-scale image coding and perception, this paper proposes a multi-scale structure-preserving heterologous image transformation method based on conditional adversarial network learning. First, using the idea of multi-scale coding and reconstruction, a multi-scale, step by step generator lightweight network structure is designed. Then, two image multi-scale structure loss functions are proposed, and combined with the existing overall consistency loss, a loss function for generative adversarial learning is designed. Finally, test experiments are performed on the KAIST-MPD-set1 dataset. The experimental results show that, compared with the state-of-the-art algorithms, the proposed algorithm can better suppress the local structural distortion, and has significant advantages in evaluation indicators such as RMSE, LPIPS, PSNR, and SSIM.
LMSeg: Language-guided Multi-dataset Segmentation
Authors: Qiang Zhou; Yuang Liu; Chaohui Yu; Jingliang Li; Zhibin Wang; Fan Wang
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: It’s a meaningful and attractive topic to build a general and inclusive segmentation model that can recognize more categories in various scenarios. A straightforward way is to combine the existing fragmented segmentation datasets and train a multi-dataset network. However, there are two major issues with multi-dataset segmentation: (i) the inconsistent taxonomy demands manual reconciliation to construct a unified taxonomy; (ii) the inflexible one-hot common taxonomy causes time-consuming model retraining and defective supervision of unlabeled categories. In this paper, we investigate the multi-dataset segmentation and propose a scalable Language-guided Multi-dataset Segmentation framework, dubbed LMSeg, which supports both semantic and panoptic segmentation. Specifically, we introduce a pretrained text encoder to map the category names to a text embedding space as a unified taxonomy, instead of using inflexible one-hot label. The model dynamically aligns the segment queries with the category embeddings. Instead of relabeling each dataset with the unified taxonomy, a category-guided decoding module is designed to dynamically guide predictions to each dataset’s taxonomy. Furthermore, we adopt a dataset-aware augmentation strategy that assigns each dataset a specific image augmentation pipeline, which can suit the proper-
ties of images from different datasets. Extensive experiments demonstrate that our method achieves significant improvements on four segmentation datasets and three panoptic datasets, while the ablation study evaluates the effectiveness of each component.
Learning with Stochastic Orders
Authors: Carles Domingo-Enrich; Yair Schiff; Youssef Mroueh
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: Learning high-dimensional distributions is often done with explicit likelihood modeling or implicit modeling via minimizing integral probability metrics (IPMs). In this paper, we expand this learning paradigm to stochastic orders, namely, the convex or Choquet order between probability measures. Towards this end, exploiting the relation between convex orders and optimal transport, we introduce the Choquet-Toland distance between probability measures, that can be used as a drop-in replacement for IPMs. We also introduce the Variational Dominance Criterion (VDC) to learn probability measures with dominance constraints, that encode the desired stochastic order between the learned measure and a known baseline. We analyze both quantities and show that they suffer from the curse of dimensionality and propose surrogates via input convex maxout networks (ICMNs), that enjoy parametric rates. We provide a min-max framework for learning with stochastic orders and validate it experimentally on synthetic and high-dimensional image generation, with promising results. Finally, our ICMNs class of convex functions and its derived Rademacher Complexity are of independent interest beyond their application in convex orders. Code to reproduce experimental results is available at https://github.com/yair-schiff/stochastic-orders-ICMN.
Go-Explore with a guide: Speeding up search in sparse reward settings with goal-directed intrinsic rewards
Authors: Chong Min John Tan; Mehul Motani
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Reinforcement Learning (RL) agents have traditionally been very sample-intensive to train, especially in environments with sparse rewards. Seeking inspiration from neuroscience experiments of rats learning the structure of a maze without needing extrinsic rewards, we seek to incorporate additional intrinsic rewards to guide behavior. We propose a potential-based goal-directed intrinsic reward (GDIR), which provides a reward signal regardless of whether the task is achieved, and ensures that learning can always take place. While GDIR may be similar to approaches such as reward shaping in incorporating goal-based rewards, we highlight that GDIR is innate to the agent and hence applicable across a wide range of environments without needing to rely on a properly shaped environment reward. We also note that GDIR is different from curiosity-based intrinsic motivation, which can diminish over time and lead to inefficient exploration. Go-Explore is a well-known state-of-the-art algorithm for sparse reward domains, and we demonstrate that by incorporating GDIR in the ``Go" and ``Explore" phases, we can improve Go-Explore's performance and enable it to learn faster across multiple environments, for both discrete (2D grid maze environments, Towers of Hanoi, Game of Nim) and continuous (Cart Pole and Mountain Car) state spaces. Furthermore, to consolidate learnt trajectories better, our method also incorporates a novel approach of hippocampal replay to update the values of GDIR and reset state visit and selection counts of states along the successful trajectory. As a benchmark, we also show that our proposed approaches learn significantly faster than traditional extrinsic-reward-based RL algorithms such as Proximal Policy Optimization, TD-learning, and Q-learning.
CAST: Concurrent Recognition and Segmentation with Adaptive Segment Tokens
Authors: Tsung-Wei Ke; Jyh-Jing Hwang; Stella Yu
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Recognizing an image and segmenting it into coherent regions are often treated as separate tasks.  Human vision, however, has a general sense of segmentation hierarchy before recognition occurs.  We are thus inspired to learn image recognition with hierarchical image segmentation based entirely on unlabeled images.  Our insight is to learn  fine-to-coarse features concurrently at superpixels, segments, and full image levels,  enforcing consistency and goodness of feature induced segmentations while maximizing discrimination among image instances.

Our model innovates vision transformers on three aspects.  1) We use adaptive segment tokens instead of fixed-shape patch tokens. 2) We create a token hierarchy by inserting graph pooling between transformer blocks, naturally producing consistent multi-scale segmentations while increasing the segment size and reducing the number of tokens.  3) We produce hierarchical image segmentation for free {\it while} training for recognition by maximizing image-wise discrimination.

Our work delivers the first concurrent recognition and hierarchical segmentation model without any supervision.   Validated on ImageNet and PASCAL VOC, it achieves better recognition and segmentation with higher computational efficiency.