  Few-shot class-incremental learning (FSCIL) aims at learning to classify new
classes continually from limited samples without forgetting the old classes.
The mainstream framework tackling FSCIL is first to adopt the cross-entropy
(CE) loss for training at the base session, then freeze the feature extractor
to adapt to new classes. However, in this work, we find that the CE loss is not
ideal for the base session training as it suffers poor class separation in
terms of representations, which further degrades generalization to novel
classes. One tempting method to mitigate this problem is to apply an additional
naive supervised contrastive learning (SCL) in the base session. Unfortunately,
we find that although SCL can create a slightly better representation
separation among different base classes, it still struggles to separate base
classes and new classes. Inspired by the observations made, we propose
Semantic-Aware Virtual Contrastive model (SAVC), a novel method that
facilitates separation between new classes and base classes by introducing
virtual classes to SCL. These virtual classes, which are generated via
pre-defined transformations, not only act as placeholders for unseen classes in
the representation space, but also provide diverse semantic information. By
learning to recognize and contrast in the fantasy space fostered by virtual
classes, our SAVC significantly boosts base class separation and novel class
generalization, achieving new state-of-the-art performance on the three
widely-used FSCIL benchmark datasets. Code is available at:
https://github.com/zysong0113/SAVC.
