  Contrastive self-supervised learning has attracted significant research
attention recently. It learns effective visual representations from unlabeled
data by embedding augmented views of the same image close to each other while
pushing away embeddings of different images. Despite its great success on
ImageNet classification, COCO object detection, etc., its performance degrades
on contrast-agnostic applications, e.g., medical image classification, where
all images are visually similar to each other. This creates difficulties in
optimizing the embedding space as the distance between images is rather small.
To solve this issue, we present the first mix-up self-supervised learning
framework for contrast-agnostic applications. We address the low variance
across images based on cross-domain mix-up and build the pretext task based on
two synergistic objectives: image reconstruction and transparency prediction.
Experimental results on two benchmark datasets validate the effectiveness of
our method, where an improvement of 2.5% ~ 7.4% in top-1 accuracy was obtained
compared to existing self-supervised learning methods.
