Search Results for author:
Found 5 papers, 5 papers with code
Fourier-basis Functions to Bridge Augmentation Gap: Rethinking Frequency Augmentation in Image Classification
However, common visual augmentations might not guarantee extensive robustness of computer vision models.
DFM-X: Augmentation by Leveraging Prior Knowledge of Shortcut Learning
We propose a data augmentation strategy, named DFM-X, that leverages knowledge about frequency shortcuts, encoded in Dominant Frequencies Maps computed for image classification models.
Defocus Blur Synthesis and Deblurring via Interpolation and Extrapolation in Latent Space
We train autoencoders with implicit and explicit regularization techniques to enforce linearity relations among the representations of different blur levels in the latent space.
What do neural networks learn in image classification? A frequency shortcut perspective
Our results demonstrate that NNs tend to find simple solutions for classification, and what they learn first during training depends on the most distinctive frequency characteristics, which can be either low- or high-frequencies.
A Survey on the Robustness of Computer Vision Models against Common Corruptions
The performance of computer vision models are susceptible to unexpected changes in input images, known as common corruptions (e. g. noise, blur, illumination changes, etc.
