Incorporating Supervised Domain Generalization into Data Augmentation

With the increasing utilization of deep learning in outdoor settings, its robustness needs to be enhanced to preserve accuracy in the face of distribution shifts, such as compression artifacts. Data augmentation is a widely used technique to improve robustness, thanks to its ease of use and numerous benefits. However, it requires more training epochs, making it difficult to train large models with limited computational resources. To address this problem, we treat data augmentation as supervised domain generalization~(SDG) and benefit from the SDG method, contrastive semantic alignment~(CSA) loss, to improve the robustness and training efficiency of data augmentation. The proposed method only adds loss during model training and can be used as a plug-in for existing data augmentation methods. Experiments on the CIFAR-100 and CUB datasets show that the proposed method improves the robustness and training efficiency of typical data augmentations.

AugNet: Dynamic Test-Time Augmentation via Differentiable Functions

Distribution shifts, which often occur in the real world, degrade the accuracy of deep learning systems, and thus improving robustness is essential for practical applications. To improve robustness, we study an image enhancement method that generates recognition-friendly images without retraining the recognition model. We propose a novel image enhancement method, AugNet, which is based on differentiable data augmentation techniques and generates a blended image from many augmented images to improve the recognition accuracy under distribution shifts. In addition to standard data augmentations, AugNet can also incorporate deep neural network-based image transformation, which further improves the robustness. Because AugNet is composed of differentiable functions, AugNet can be directly trained with the classification loss of the recognition model. AugNet is evaluated on widely used image recognition datasets using various classification models, including Vision Transformer and MLP-Mixer. AugNet improves the robustness with almost no reduction in classification accuracy for clean images, which is a better result than the existing methods. Furthermore, we show that interpretation of distribution shifts using AugNet and retraining based on that interpretation can greatly improve robustness.