Characterizing Model Robustness via Natural Input Gradients

• URL: http://arxiv.org/abs/2409.20139v1
• Date: Mon, 30 Sep 2024 09:41:34 GMT
• Title: Characterizing Model Robustness via Natural Input Gradients
• Abstract summary: We show the surprising effectiveness of instead regularizing the gradient with respect to model inputs on natural examples only. On ImageNet-1k, Gradient Norm training achieves > 90% the performance of state-of-the-art PGD-3 Adversarial Training (52% vs.56%), while using only 60% cost of the state-of-the-art without complex adversarial optimization.
• Score: 37.97521090347974
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Adversarially robust models are locally smooth around each data sample so that small perturbations cannot drastically change model outputs. In modern systems, such smoothness is usually obtained via Adversarial Training, which explicitly enforces models to perform well on perturbed examples. In this work, we show the surprising effectiveness of instead regularizing the gradient with respect to model inputs on natural examples only. Penalizing input Gradient Norm is commonly believed to be a much inferior approach. Our analyses identify that the performance of Gradient Norm regularization critically depends on the smoothness of activation functions, and are in fact extremely effective on modern vision transformers that adopt smooth activations over piecewise linear ones (eg, ReLU), contrary to prior belief. On ImageNet-1k, Gradient Norm training achieves > 90% the performance of state-of-the-art PGD-3 Adversarial Training} (52% vs.~56%), while using only 60% computation cost of the state-of-the-art without complex adversarial optimization. Our analyses also highlight the relationship between model robustness and properties of natural input gradients, such as asymmetric sample and channel statistics. Surprisingly, we find model robustness can be significantly improved by simply regularizing its gradients to concentrate on image edges without explicit conditioning on the gradient norm.

Related papers

• Need We Teach Foundation Models What is a Generative Image? Gradient-Free Generative Artifact Detection via Analytic Spectral Adaptation [3.881211374324378]
  Adapting foundation models to detect generative artifacts via gradient-based updates compromises their intrinsic representations.<n>We propose a gradient-free methodology reframing detection from binary classification to an out-of-distribution (OOD) anomaly measurement problem.
  arXiv  Detail & Related papers  (2026-06-03T13:04:23Z)
• Dynamic Scaled Gradient Descent for Stable Fine-Tuning for Classifications [0.9174244628582179]
  We propose a novel algorithm that modifies the gradients returned by training examples, specifically, scaling down the gradients of correctly classified examples using a dynamic scaler.<n>Experiments on a variety of benchmark datasets, spanning multiple tasks and large pretrained models, demonstrate that our method consistently reduces performance variance and surpasses the accuracy of existing approaches.
  arXiv  Detail & Related papers  (2026-04-30T15:16:00Z)
• Attribution-Guided Model Rectification of Unreliable Neural Network Behaviors [60.06461883533697]
  We leverage rank-one model editing to establish an attribution-guided model rectification framework.<n>We first distinguish our rectification setting from existing model editing, yielding a formulation that corrects unreliable behavior.<n>To target the primary source of misbehavior, we introduce an attribution-guided layer localization method.
  arXiv  Detail & Related papers  (2026-03-08T01:06:07Z)
• Parameter-Efficient Conditioning for Material Generalization in Graph-Based Simulators [2.504298819189614]
  Graph network-based simulators (GNS) have demonstrated strong potential for learning particle-based physics.<n>Existing models are typically trained for a single material type and fail to generalize across distinct behaviors.<n>We propose a parameter-efficient conditioning mechanism that makes the GNS model adaptive to material parameters.
  arXiv  Detail & Related papers  (2025-11-07T17:55:35Z)
• Solving Inverse Problems with FLAIR [59.02385492199431]
  Flow-based latent generative models are able to generate images with remarkable quality, even enabling text-to-image generation.<n>We present FLAIR, a novel training free variational framework that leverages flow-based generative models as a prior for inverse problems.<n>Results on standard imaging benchmarks demonstrate that FLAIR consistently outperforms existing diffusion- and flow-based methods in terms of reconstruction quality and sample diversity.
  arXiv  Detail & Related papers  (2025-06-03T09:29:47Z)
• COME: Test-time adaption by Conservatively Minimizing Entropy [45.689829178140634]
  Conservatively Minimize the Entropy (COME) is a drop-in replacement of traditional entropy (EM) COME explicitly models the uncertainty by characterizing a Dirichlet prior distribution over model predictions. We show that COME achieves state-of-the-art performance on commonly used benchmarks.
  arXiv  Detail & Related papers  (2024-10-12T09:20:06Z)
• Regulating Model Reliance on Non-Robust Features by Smoothing Input Marginal Density [93.32594873253534]
  Trustworthy machine learning requires meticulous regulation of model reliance on non-robust features. We propose a framework to delineate and regulate such features by attributing model predictions to the input.
  arXiv  Detail & Related papers  (2024-07-05T09:16:56Z)
• Scaling and renormalization in high-dimensional regression [72.59731158970894]
  We present a unifying perspective on recent results on ridge regression.<n>We use the basic tools of random matrix theory and free probability, aimed at readers with backgrounds in physics and deep learning.<n>Our results extend and provide a unifying perspective on earlier models of scaling laws.
  arXiv  Detail & Related papers  (2024-05-01T15:59:00Z)
• Model-Based Reparameterization Policy Gradient Methods: Theory and Practical Algorithms [88.74308282658133]
  Reization (RP) Policy Gradient Methods (PGMs) have been widely adopted for continuous control tasks in robotics and computer graphics. Recent studies have revealed that, when applied to long-term reinforcement learning problems, model-based RP PGMs may experience chaotic and non-smooth optimization landscapes. We propose a spectral normalization method to mitigate the exploding variance issue caused by long model unrolls.
  arXiv  Detail & Related papers  (2023-10-30T18:43:21Z)
• Understanding the robustness difference between stochastic gradient descent and adaptive gradient methods [11.895321856533934]
  gradient descent (SGD) and adaptive gradient methods have been widely used in training deep neural networks. We empirically show that while the difference between the standard generalization performance of models trained using these methods is small, those trained using SGD exhibit far greater robustness under input perturbations.
  arXiv  Detail & Related papers  (2023-08-13T07:03:22Z)
• Selectively Hard Negative Mining for Alleviating Gradient Vanishing in Image-Text Matching [15.565068934153983]
  Most existing Image-Text Matching (ITM) models suffer from gradients vanishing at the beginning of training. We propose a Selectively Hard Negative Mining (SelHN) strategy, which chooses whether to mine hard negative samples. SelHN can be plug-and-play applied to existing ITM models to give them better training behavior.
  arXiv  Detail & Related papers  (2023-03-01T02:15:07Z)
• Relating Regularization and Generalization through the Intrinsic Dimension of Activations [11.00580615194563]
  We show that common regularization techniques uniformly decrease the last-layer ID (LLID) of validation set activations for image classification models. We also examine the LLID over the course of training of models that exhibit grokking.
  arXiv  Detail & Related papers  (2022-11-23T19:00:00Z)
• MEMO: Test Time Robustness via Adaptation and Augmentation [131.28104376280197]
  We study the problem of test time robustification, i.e., using the test input to improve model robustness. Recent prior works have proposed methods for test time adaptation, however, they each introduce additional assumptions. We propose a simple approach that can be used in any test setting where the model is probabilistic and adaptable.
  arXiv  Detail & Related papers  (2021-10-18T17:55:11Z)
• Goal-directed Generation of Discrete Structures with Conditional Generative Models [85.51463588099556]
  We introduce a novel approach to directly optimize a reinforcement learning objective, maximizing an expected reward. We test our methodology on two tasks: generating molecules with user-defined properties and identifying short python expressions which evaluate to a given target value.
  arXiv  Detail & Related papers  (2020-10-05T20:03:13Z)
• Extrapolation for Large-batch Training in Deep Learning [72.61259487233214]
  We show that a host of variations can be covered in a unified framework that we propose. We prove the convergence of this novel scheme and rigorously evaluate its empirical performance on ResNet, LSTM, and Transformer.
  arXiv  Detail & Related papers  (2020-06-10T08:22:41Z)

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.