  Whilst contrastive learning yields powerful representations by matching
different augmented views of the same instance, it lacks the ability to capture
the similarities between different instances. One popular way to address this
limitation is by learning global features (after the global pooling) to capture
inter-instance relationships based on knowledge distillation, where the global
features of the teacher are used to guide the learning of the global features
of the student. Inspired by cross-modality learning, we extend this existing
framework that only learns from global features by encouraging the global
features and intermediate layer features to learn from each other. This leads
to our novel self-supervised framework: cross-context learning between global
and hypercolumn features (CGH), that enforces the consistency of instance
relations between low- and high-level semantics. Specifically, we stack the
intermediate feature maps to construct a hypercolumn representation so that we
can measure instance relations using two contexts (hypercolumn and global
feature) separately, and then use the relations of one context to guide the
learning of the other. This cross-context learning allows the model to learn
from the differences between the two contexts. The experimental results on
linear classification and downstream tasks show that our method outperforms the
state-of-the-art methods.
