Multiplicative Reweighting for Robust Neural Network Optimization

• URL: http://arxiv.org/abs/2102.12192v4
• Date: Sun, 26 May 2024 12:27:35 GMT
• Title: Multiplicative Reweighting for Robust Neural Network Optimization
• Authors: Noga Bar, Tomer Koren, Raja Giryes,
• Abstract summary: Multiplicative weight (MW) updates are robust to moderate data corruptions in expert advice. We show that MW improves the accuracy of neural networks in the presence of label noise.
• Score: 51.67267839555836
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Neural networks are widespread due to their powerful performance. However, they degrade in the presence of noisy labels at training time. Inspired by the setting of learning with expert advice, where multiplicative weight (MW) updates were recently shown to be robust to moderate data corruptions in expert advice, we propose to use MW for reweighting examples during neural networks optimization. We theoretically establish the convergence of our method when used with gradient descent and prove its advantages in 1d cases. We then validate our findings empirically for the general case by showing that MW improves the accuracy of neural networks in the presence of label noise on CIFAR-10, CIFAR-100 and Clothing1M. We also show the impact of our approach on adversarial robustness.

Related papers

• 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)
• Latent Feature Relation Consistency for Adversarial Robustness [80.24334635105829]
  misclassification will occur when deep neural networks predict adversarial examples which add human-imperceptible adversarial noise to natural examples. We propose textbfLatent textbfFeature textbfRelation textbfConsistency (textbfLFRC) LFRC constrains the relation of adversarial examples in latent space to be consistent with the natural examples.
  arXiv  Detail & Related papers  (2023-03-29T13:50:01Z)
• Frequency Regularization for Improving Adversarial Robustness [8.912245110734334]
  adversarial training (AT) has proven to be an effective defense approach. We propose a frequency regularization (FR) to align the output difference in the spectral domain. We find that our method achieves the strongest robustness against attacks by PGD-20, C&W and Autoattack.
  arXiv  Detail & Related papers  (2022-12-24T13:14:45Z)
• CorrectNet: Robustness Enhancement of Analog In-Memory Computing for Neural Networks by Error Suppression and Compensation [4.570841222958966]
  We propose a framework to enhance the robustness of neural networks under variations and noise. We show that inference accuracy of neural networks can be recovered from as low as 1.69% under variations and noise.
  arXiv  Detail & Related papers  (2022-11-27T19:13:33Z)
• On the optimization and pruning for Bayesian deep learning [1.0152838128195467]
  We propose a new adaptive variational Bayesian algorithm to train neural networks on weight space. The EM-MCMC algorithm allows us to perform optimization and model pruning within one-shot. Our dense model can reach the state-of-the-art performance and our sparse model perform very well compared to previously proposed pruning schemes.
  arXiv  Detail & Related papers  (2022-10-24T05:18:08Z)
• WeightMom: Learning Sparse Networks using Iterative Momentum-based pruning [0.0]
  We propose a weight based pruning approach in which the weights are pruned gradually based on their momentum of the previous iterations. We evaluate our approach on networks such as AlexNet, VGG16 and ResNet50 with image classification datasets such as CIFAR-10 and CIFAR-100.
  arXiv  Detail & Related papers  (2022-08-11T07:13:59Z)
• Can pruning improve certified robustness of neural networks? [106.03070538582222]
  We show that neural network pruning can improve empirical robustness of deep neural networks (NNs) Our experiments show that by appropriately pruning an NN, its certified accuracy can be boosted up to 8.2% under standard training. We additionally observe the existence of certified lottery tickets that can match both standard and certified robust accuracies of the original dense models.
  arXiv  Detail & Related papers  (2022-06-15T05:48:51Z)
• Semantic Perturbations with Normalizing Flows for Improved Generalization [62.998818375912506]
  We show that perturbations in the latent space can be used to define fully unsupervised data augmentations. We find that our latent adversarial perturbations adaptive to the classifier throughout its training are most effective.
  arXiv  Detail & Related papers  (2021-08-18T03:20:00Z)
• Training Sparse Neural Networks using Compressed Sensing [13.84396596420605]
  We develop and test a novel method based on compressed sensing which combines the pruning and training into a single step. Specifically, we utilize an adaptively weighted $ell1$ penalty on the weights during training, which we combine with a generalization of the regularized dual averaging (RDA) algorithm in order to train sparse neural networks.
  arXiv  Detail & Related papers  (2020-08-21T19:35:54Z)
• Neural networks with late-phase weights [66.72777753269658]
  We show that the solutions found by SGD can be further improved by ensembling a subset of the weights in late stages of learning. At the end of learning, we obtain back a single model by taking a spatial average in weight space.
  arXiv  Detail & Related papers  (2020-07-25T13:23:37Z)

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.