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Found 6 papers, 4 papers with code
Toward Learning Robust and Invariant Representations with Alignment Regularization and Data Augmentation
Finally, we test this simple technique we identify (worst-case data augmentation with squared l2 norm alignment regularization) and show that the benefits of this method outrun those of the specially designed methods.
Ego4D: Around the World in 3,000 Hours of Egocentric Video
We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite.
On the Consistency Loss for Leveraging Augmented Data to Learn Robust and Invariant Representations
Data augmentation is one of the most popular techniques for improving the robustness of neural networks.
Squared $\ell_2$ Norm as Consistency Loss for Leveraging Augmented Data to Learn Robust and Invariant Representations
Data augmentation is one of the most popular techniques for improving the robustness of neural networks.
Smooth Kernels Improve Adversarial Robustness and Perceptually-Aligned Gradients
Recent research has shown that CNNs are often overly sensitive to high-frequency textural patterns.
High Frequency Component Helps Explain the Generalization of Convolutional Neural Networks
We investigate the relationship between the frequency spectrum of image data and the generalization behavior of convolutional neural networks (CNN).
