  Novel classes frequently arise in our dynamically changing world, e.g., new
users in the authentication system, and a machine learning model should
recognize new classes without forgetting old ones. This scenario becomes more
challenging when new class instances are insufficient, which is called few-shot
class-incremental learning (FSCIL). Current methods handle incremental learning
retrospectively by making the updated model similar to the old one. By
contrast, we suggest learning prospectively to prepare for future updates, and
propose ForwArd Compatible Training (FACT) for FSCIL. Forward compatibility
requires future new classes to be easily incorporated into the current model
based on the current stage data, and we seek to realize it by reserving
embedding space for future new classes. In detail, we assign virtual prototypes
to squeeze the embedding of known classes and reserve for new ones. Besides, we
forecast possible new classes and prepare for the updating process. The virtual
prototypes allow the model to accept possible updates in the future, which act
as proxies scattered among embedding space to build a stronger classifier
during inference. FACT efficiently incorporates new classes with forward
compatibility and meanwhile resists forgetting of old ones. Extensive
experiments validate FACT's state-of-the-art performance. Code is available at:
https://github.com/zhoudw-zdw/CVPR22-Fact
