We propose dversarial eature ugmentation and ormalization (A-FAN), which ( ) first augments visual recognition models with adversarial features that integrate flexible scales of perturbation strengths, ( ) then extracts adversarial feature statistics from batch normalization, and re-injects them into clean features ...
Apr 27, 2022
Mar 22, 2021 · We propose Adversarial Feature Augmentation and Normalization (A-FAN), which (i) first augments visual recognition models with adversarial features.
More detailed results of performancing A-FAN on visual recognition tasks (eg, classification, detection, segmentation) are referred to our paper here.
Recent advances in computer vision take advantage of adversarial data augmentation to improve the generalization of classification models.
Nov 27, 2022 · Add a description, image, and links to the adversarial-feature-normalization topic page so that developers can more easily learn about it.
Jan 1, 2021 · Adversarial training is an effective method to combat adversarial attacks in order to create robust neural networks.
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A novel method to train deep neural networks for small-scale training dataset by adversarial augmenting data at an input layer by adding small perturbations ...
We demonstrate its efficacy across several recognition benchmark data sets where it improves the generalization capability of highly competitive baseline.
Aug 23, 2024 · To unleash the potential of fixed deep features, we propose a novel semantic adversarial augmentation (SeA) in the fea- ture space for ...
We develop a novel adversarial feature augmentation (AFA) method for dis- tribution alignment without accessing to the target domain data. During adversarial ...