TransformMix: Learning Transformation and Mixing Strategies from Data
Tsz-Him Cheung Dit-Yan Yeung
Data augmentation improves the generalization power of deep learning models
by synthesizing more training samples. Sample-mixing is a popular data
augmentation approach that creates additional data by combining existing
samples. Recent sample-mixing methods, like Mixup and Cutmix, adopt simple
mixing operations to blend multiple inputs. Although such a heuristic approach
shows certain performance gains in some computer vision tasks, it mixes the
images blindly and does not adapt to different datasets automatically. A mixing
strategy that is effective for a particular dataset does not often generalize
well to other datasets. If not properly configured, the methods may create
misleading mixed images, which jeopardize the effectiveness of sample-mixing
augmentations. In this work, we propose an automated approach, TransformMix, to
learn better transformation and mixing augmentation strategies from data. In
particular, TransformMix applies learned transformations and mixing masks to
create compelling mixed images that contain correct and important information
for the target tasks. We demonstrate the effectiveness of TransformMix on
multiple datasets in transfer learning, classification, object detection, and
knowledge distillation settings. Experimental results show that our method
achieves better performance as well as efficiency when compared with strong
sample-mixing baselines.