A Unified Contrastive Energy-based Model for Understanding the Generative Ability of Adversarial Training

• URL: http://arxiv.org/abs/2203.13455v1
• Date: Fri, 25 Mar 2022 05:33:34 GMT
• Title: A Unified Contrastive Energy-based Model for Understanding the Generative Ability of Adversarial Training
• Authors: Yifei Wang, Yisen Wang, Jiansheng Yang, Zhouchen Lin
• Abstract summary: Adversarial Training (AT) is an effective approach to enhance the robustness of deep neural networks. We demystify this phenomenon by developing a unified probabilistic framework, called Contrastive Energy-based Models (CEM) We propose a principled method to develop adversarial learning and sampling methods.
• Score: 64.71254710803368
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Adversarial Training (AT) is known as an effective approach to enhance the robustness of deep neural networks. Recently researchers notice that robust models with AT have good generative ability and can synthesize realistic images, while the reason behind it is yet under-explored. In this paper, we demystify this phenomenon by developing a unified probabilistic framework, called Contrastive Energy-based Models (CEM). On the one hand, we provide the first probabilistic characterization of AT through a unified understanding of robustness and generative ability. On the other hand, our unified framework can be extended to the unsupervised scenario, which interprets unsupervised contrastive learning as an important sampling of CEM. Based on these, we propose a principled method to develop adversarial learning and sampling methods. Experiments show that the sampling methods derived from our framework improve the sample quality in both supervised and unsupervised learning. Notably, our unsupervised adversarial sampling method achieves an Inception score of 9.61 on CIFAR-10, which is superior to previous energy-based models and comparable to state-of-the-art generative models.

Related papers

• Idempotent Unsupervised Representation Learning for Skeleton-Based Action Recognition [13.593511876719367]
  We propose a novel skeleton-based idempotent generative model (IGM) for unsupervised representation learning. Our experiments on benchmark datasets, NTU RGB+D and PKUMMD, demonstrate the effectiveness of our proposed method.
  arXiv  Detail & Related papers  (2024-10-27T06:29:04Z)
• A Bayesian Unification of Self-Supervised Clustering and Energy-Based Models [11.007541337967027]
  We perform a Bayesian analysis of state-of-the-art self-supervised learning objectives. We show that our objective function allows to outperform existing self-supervised learning strategies. We also demonstrate that GEDI can be integrated into a neuro-symbolic framework.
  arXiv  Detail & Related papers  (2023-12-30T04:46:16Z)
• A Novel Neural-symbolic System under Statistical Relational Learning [50.747658038910565]
  We propose a general bi-level probabilistic graphical reasoning framework called GBPGR. In GBPGR, the results of symbolic reasoning are utilized to refine and correct the predictions made by the deep learning models. Our approach achieves high performance and exhibits effective generalization in both transductive and inductive tasks.
  arXiv  Detail & Related papers  (2023-09-16T09:15:37Z)
• RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment [32.752633250862694]
  Generative foundation models are susceptible to implicit biases that can arise from extensive unsupervised training data. We introduce a new framework, Reward rAnked FineTuning, designed to align generative models effectively.
  arXiv  Detail & Related papers  (2023-04-13T18:22:40Z)
• CoopInit: Initializing Generative Adversarial Networks via Cooperative Learning [50.90384817689249]
  CoopInit is a cooperative learning-based strategy that can quickly learn a good starting point for GANs. We demonstrate the effectiveness of the proposed approach on image generation and one-sided unpaired image-to-image translation tasks.
  arXiv  Detail & Related papers  (2023-03-21T07:49:32Z)
• Latent Variable Representation for Reinforcement Learning [131.03944557979725]
  It remains unclear theoretically and empirically how latent variable models may facilitate learning, planning, and exploration to improve the sample efficiency of model-based reinforcement learning. We provide a representation view of the latent variable models for state-action value functions, which allows both tractable variational learning algorithm and effective implementation of the optimism/pessimism principle. In particular, we propose a computationally efficient planning algorithm with UCB exploration by incorporating kernel embeddings of latent variable models.
  arXiv  Detail & Related papers  (2022-12-17T00:26:31Z)
• Bayesian Learning with Information Gain Provably Bounds Risk for a Robust Adversarial Defense [27.545466364906773]
  We present a new algorithm to learn a deep neural network model robust against adversarial attacks. Our model demonstrate significantly improved robustness--up to 20%--compared with adversarial training and Adv-BNN under PGD attacks.
  arXiv  Detail & Related papers  (2022-12-05T03:26:08Z)
• Learnability of Competitive Threshold Models [11.005966612053262]
  We study the learnability of the competitive threshold model from a theoretical perspective. We demonstrate how competitive threshold models can be seamlessly simulated by artificial neural networks.
  arXiv  Detail & Related papers  (2022-05-08T01:11:51Z)
• Improving White-box Robustness of Pre-processing Defenses via Joint Adversarial Training [106.34722726264522]
  A range of adversarial defense techniques have been proposed to mitigate the interference of adversarial noise. Pre-processing methods may suffer from the robustness degradation effect. A potential cause of this negative effect is that adversarial training examples are static and independent to the pre-processing model. We propose a method called Joint Adversarial Training based Pre-processing (JATP) defense.
  arXiv  Detail & Related papers  (2021-06-10T01:45:32Z)
• On the model-based stochastic value gradient for continuous reinforcement learning [50.085645237597056]
  We show that simple model-based agents can outperform state-of-the-art model-free agents in terms of both sample-efficiency and final reward. Our findings suggest that model-based policy evaluation deserves closer attention.
  arXiv  Detail & Related papers  (2020-08-28T17:58:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.