  Recent advances in computer vision take advantage of adversarial data
augmentation to ameliorate the generalization ability of classification models.
Here, we present an effective and efficient alternative that advocates
adversarial augmentation on intermediate feature embeddings, instead of relying
on computationally-expensive pixel-level perturbations. We propose Adversarial
Feature Augmentation and Normalization (A-FAN), which (i) first augments visual
recognition models with adversarial features that integrate flexible scales of
perturbation strengths, (ii) then extracts adversarial feature statistics from
batch normalization, and re-injects them into clean features through feature
normalization. We validate the proposed approach across diverse visual
recognition tasks with representative backbone networks, including ResNets and
EfficientNets for classification, Faster-RCNN for detection, and Deeplab V3+
for segmentation. Extensive experiments show that A-FAN yields consistent
generalization improvement over strong baselines across various datasets for
classification, detection and segmentation tasks, such as CIFAR-10, CIFAR-100,
ImageNet, Pascal VOC2007, Pascal VOC2012, COCO2017, and Cityspaces.
Comprehensive ablation studies and detailed analyses also demonstrate that
adding perturbations to specific modules and layers of
classification/detection/segmentation backbones yields optimal performance.
Codes and pre-trained models will be made available at:
https://github.com/VITA-Group/CV_A-FAN.
