  Contrastive learning, which aims at minimizing the distance between positive
pairs while maximizing that of negative ones, has been widely and successfully
applied in unsupervised feature learning, where the design of positive and
negative (pos/neg) pairs is one of its keys. In this paper, we attempt to
devise a feature-level data manipulation, differing from data augmentation, to
enhance the generic contrastive self-supervised learning. To this end, we first
design a visualization scheme for pos/neg score (Pos/neg score indicates cosine
similarity of pos/neg pair.) distribution, which enables us to analyze,
interpret and understand the learning process. To our knowledge, this is the
first attempt of its kind. More importantly, leveraging this tool, we gain some
significant observations, which inspire our novel Feature Transformation
proposals including the extrapolation of positives. This operation creates
harder positives to boost the learning because hard positives enable the model
to be more view-invariant. Besides, we propose the interpolation among
negatives, which provides diversified negatives and makes the model more
discriminative. It is the first attempt to deal with both challenges
simultaneously. Experiment results show that our proposed Feature
Transformation can improve at least 6.0% accuracy on ImageNet-100 over MoCo
baseline, and about 2.0% accuracy on ImageNet-1K over the MoCoV2 baseline.
Transferring to the downstream tasks successfully demonstrate our model is less
task-bias. Visualization tools and codes
https://github.com/DTennant/CL-Visualizing-Feature-Transformation .
