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Found 10 papers, 1 papers with code
Give Me Your Attention: Dot-Product Attention Considered Harmful for Adversarial Patch Robustness
Neural architectures based on attention such as vision transformers are revolutionizing image recognition.
DiagViB-6: A Diagnostic Benchmark Suite for Vision Models in the Presence of Shortcut and Generalization Opportunities
We also argue that it is necessary for DNNs to exploit GO to overcome shortcut learning.
Test-Time Adaptation to Distribution Shift by Confidence Maximization and Input Transformation
This paper focuses on the fully test-time adaptation setting, where only unlabeled data from the target distribution is required.
Test-Time Adaptation to Distribution Shifts by Confidence Maximization and Input Transformation
This paper focuses on the fully test-time adaptation setting, where only unlabeled data from the target distribution is required.
Does enhanced shape bias improve neural network robustness to common corruptions?
We conclude that the data augmentation caused by style-variation accounts for the improved corruption robustness and increased shape bias is only a byproduct.
DeepUSPS: Deep Robust Unsupervised Saliency Prediction via Self-supervision
Alternative unsupervised approaches rely on careful selection of multiple handcrafted saliency methods to generate noisy pseudo-ground-truth labels.
SELF: Learning to Filter Noisy Labels with Self-Ensembling
Deep neural networks (DNNs) have been shown to over-fit a dataset when being trained with noisy labels for a long enough time.
DeepUSPS: Deep Robust Unsupervised Saliency Prediction With Self-Supervision
Alternative unsupervised approaches rely on careful selection of multiple handcrafted saliency methods to generate noisy pseudo-ground-truth labels.
Group Pruning using a Bounded-Lp norm for Group Gating and Regularization
We achieve state-of-the-art pruning results for ResNet-50 with higher accuracy on ImageNet.
Defending Against Universal Perturbations With Shared Adversarial Training
Classifiers such as deep neural networks have been shown to be vulnerable against adversarial perturbations on problems with high-dimensional input space.
