October 20, 2019
Occlusions for Effective Data Augmentation in Image Classification
ICCV Workshop on Interpreting and Explaining Visual AI Models
In this paper, we show that, by using a simple technique based on batch augmentation, occlusions as data augmentation can result in better performance on ImageNet for high-capacity models (e.g., ResNet50). We also show that varying amounts of occlusions used during training can be used to study the robustness of different neural network architectures.
By: Ruth Fong, Andrea Vedaldi