Self-Supervised Dynamic Networks for Covariate Shift Robustness

• URL: http://arxiv.org/abs/2006.03952v1
• Date: Sat, 6 Jun 2020 19:37:20 GMT
• Title: Self-Supervised Dynamic Networks for Covariate Shift Robustness
• Authors: Tomer Cohen, Noy Shulman, Hai Morgenstern, Roey Mechrez, and Erez Farhan
• Abstract summary: Self-Supervised Dynamic Networks (SSDN) is an input-dependent mechanism that allows a self-supervised network to predict the weights of the main network. We present the conceptual and empirical advantages of the proposed method on the problem of image classification.
• Score: 9.542023122304098
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As supervised learning still dominates most AI applications, test-time performance is often unexpected. Specifically, a shift of the input covariates, caused by typical nuisances like background-noise, illumination variations or transcription errors, can lead to a significant decrease in prediction accuracy. Recently, it was shown that incorporating self-supervision can significantly improve covariate shift robustness. In this work, we propose Self-Supervised Dynamic Networks (SSDN): an input-dependent mechanism, inspired by dynamic networks, that allows a self-supervised network to predict the weights of the main network, and thus directly handle covariate shifts at test-time. We present the conceptual and empirical advantages of the proposed method on the problem of image classification under different covariate shifts, and show that it significantly outperforms comparable methods.

Related papers

• Beyond Pruning Criteria: The Dominant Role of Fine-Tuning and Adaptive Ratios in Neural Network Robustness [7.742297876120561]
  Deep neural networks (DNNs) excel in tasks like image recognition and natural language processing. Traditional pruning methods compromise the network's ability to withstand subtle perturbations. This paper challenges the conventional emphasis on weight importance scoring as the primary determinant of a pruned network's performance.
  arXiv  Detail & Related papers  (2024-10-19T18:35:52Z)
• Improving Network Interpretability via Explanation Consistency Evaluation [56.14036428778861]
  We propose a framework that acquires more explainable activation heatmaps and simultaneously increase the model performance. Specifically, our framework introduces a new metric, i.e., explanation consistency, to reweight the training samples adaptively in model learning. Our framework then promotes the model learning by paying closer attention to those training samples with a high difference in explanations.
  arXiv  Detail & Related papers  (2024-08-08T17:20:08Z)
• Leveraging Low-Rank and Sparse Recurrent Connectivity for Robust Closed-Loop Control [63.310780486820796]
  We show how a parameterization of recurrent connectivity influences robustness in closed-loop settings. We find that closed-form continuous-time neural networks (CfCs) with fewer parameters can outperform their full-rank, fully-connected counterparts.
  arXiv  Detail & Related papers  (2023-10-05T21:44:18Z)
• Deep Neural Networks Tend To Extrapolate Predictably [51.303814412294514]
  neural network predictions tend to be unpredictable and overconfident when faced with out-of-distribution (OOD) inputs. We observe that neural network predictions often tend towards a constant value as input data becomes increasingly OOD. We show how one can leverage our insights in practice to enable risk-sensitive decision-making in the presence of OOD inputs.
  arXiv  Detail & Related papers  (2023-10-02T03:25:32Z)
• Addressing Mistake Severity in Neural Networks with Semantic Knowledge [0.0]
  Most robust training techniques aim to improve model accuracy on perturbed inputs. As an alternate form of robustness, we aim to reduce the severity of mistakes made by neural networks in challenging conditions. We leverage current adversarial training methods to generate targeted adversarial attacks during the training process. Results demonstrate that our approach performs better with respect to mistake severity compared to standard and adversarially trained models.
  arXiv  Detail & Related papers  (2022-11-21T22:01:36Z)
• Improved and Interpretable Defense to Transferred Adversarial Examples by Jacobian Norm with Selective Input Gradient Regularization [31.516568778193157]
  Adversarial training (AT) is often adopted to improve the robustness of deep neural networks (DNNs) In this work, we propose an approach based on Jacobian norm and Selective Input Gradient Regularization (J- SIGR) Experiments demonstrate that the proposed J- SIGR confers improved robustness against transferred adversarial attacks, and we also show that the predictions from the neural network are easy to interpret.
  arXiv  Detail & Related papers  (2022-07-09T01:06:41Z)
• Hindsight Network Credit Assignment: Efficient Credit Assignment in Networks of Discrete Stochastic Units [2.28438857884398]
  We present Hindsight Network Credit Assignment (HNCA), a novel learning algorithm for networks of discrete units. HNCA works by assigning credit to each unit based on the degree to which its output influences its immediate children in the network. We show how HNCA can be extended to optimize a more general function of the outputs of a network of units, where the function is known to the agent.
  arXiv  Detail & Related papers  (2021-10-14T20:18:38Z)
• Bayesian Attention Belief Networks [59.183311769616466]
  Attention-based neural networks have achieved state-of-the-art results on a wide range of tasks. This paper introduces Bayesian attention belief networks, which construct a decoder network by modeling unnormalized attention weights. We show that our method outperforms deterministic attention and state-of-the-art attention in accuracy, uncertainty estimation, generalization across domains, and adversarial attacks.
  arXiv  Detail & Related papers  (2021-06-09T17:46:22Z)
• Non-Singular Adversarial Robustness of Neural Networks [58.731070632586594]
  Adrial robustness has become an emerging challenge for neural network owing to its over-sensitivity to small input perturbations. We formalize the notion of non-singular adversarial robustness for neural networks through the lens of joint perturbations to data inputs as well as model weights.
  arXiv  Detail & Related papers  (2021-02-23T20:59:30Z)
• Improve Generalization and Robustness of Neural Networks via Weight Scale Shifting Invariant Regularizations [52.493315075385325]
  We show that a family of regularizers, including weight decay, is ineffective at penalizing the intrinsic norms of weights for networks with homogeneous activation functions. We propose an improved regularizer that is invariant to weight scale shifting and thus effectively constrains the intrinsic norm of a neural network.
  arXiv  Detail & Related papers  (2020-08-07T02:55:28Z)
• Defense Through Diverse Directions [24.129270094757587]
  We develop a novel Bayesian neural network methodology to achieve strong adversarial robustness. We demonstrate that by encouraging the network to distribute evenly across inputs, the network becomes less susceptible to localized, brittle features. We show empirical robustness on several benchmark datasets.
  arXiv  Detail & Related papers  (2020-03-24T01:22:03Z)

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.