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Found 8 papers, 6 papers with code
Modularity Trumps Invariance for Compositional Robustness
In this work we develop a compositional image classification task where, given a few elemental corruptions, models are asked to generalize to compositions of these corruptions.
Deephys: Deep Electrophysiology, Debugging Neural Networks under Distribution Shifts
Deep Neural Networks (DNNs) often fail in out-of-distribution scenarios.
Adversarial Robustness without Adversarial Training: A Teacher-Guided Curriculum Learning Approach
Current SOTA adversarially robust models are mostly based on adversarial training (AT) and differ only by some regularizers either at inner maximization or outer minimization steps.
Get Fooled for the Right Reason: Improving Adversarial Robustness through a Teacher-guided Curriculum Learning Approach
Current SOTA adversarially robust models are mostly based on adversarial training (AT) and differ only by some regularizers either at inner maximization or outer minimization steps.
A Framework for Learning Ante-hoc Explainable Models via Concepts
To the best of our knowledge, we are the first ante-hoc explanation generation method to show results with a large-scale dataset such as ImageNet.
Enhanced Regularizers for Attributional Robustness
Deep neural networks are the default choice of learning models for computer vision tasks.
Neural Network Attributions: A Causal Perspective
We propose a new attribution method for neural networks developed using first principles of causality (to the best of our knowledge, the first such).
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
Over the last decade, Convolutional Neural Network (CNN) models have been highly successful in solving complex vision problems.
Ranked #1 on
Error Understanding
on CUB-200-2011 (ResNet-101)
