In computer vision, contrastive learning is the most advanced unsupervised
learning framework. Yet most previous methods simply apply fixed composition of
data augmentations to improve data efficiency, which ignores the changes in
their optimal settings over training. Thus, the pre-determined parameters of
augmentation operations cannot always fit well with an evolving network during
the whole training period, which degrades the quality of the learned
representations. In this work, we propose AdDA, which implements a closed-loop
feedback structure to a generic contrastive learning network. AdDA works by
allowing the network to adaptively adjust the augmentation compositions
according to the real-time feedback. This online adjustment helps maintain the
dynamic optimal composition and enables the network to acquire more
generalizable representations with minimal computational overhead. AdDA
achieves competitive results under the common linear protocol on ImageNet-100
classification (+1.11% on MoCo v2).

本研究提出了一种名为 AdDA 的自适应数据增强（Adaptive Data Augmentation）方法，通过在训练过程中实时反馈调整数据增强的组合，优化对比学习网络的表示学习效果，并在 ImageNet-100 数据集上进行了测试。