  Few-shot class-incremental learning (FSCIL) presents the primary challenge of
balancing underfitting to a new session's task and forgetting the tasks from
previous sessions. To address this challenge, we develop a simple yet powerful
learning scheme that integrates effective methods for each core component of
the FSCIL network, including the feature extractor, base session classifiers,
and incremental session classifiers. In feature extractor training, our goal is
to obtain balanced generic representations that benefit both current viewable
and unseen or past classes. To achieve this, we propose a balanced supervised
contrastive loss that effectively balances these two objectives. In terms of
classifiers, we analyze and emphasize the importance of unifying initialization
methods for both the base and incremental session classifiers. Our method
demonstrates outstanding ability for new task learning and preventing
forgetting on CUB200, CIFAR100, and miniImagenet datasets, with significant
improvements over previous state-of-the-art methods across diverse metrics. We
conduct experiments to analyze the significance and rationale behind our
approach and visualize the effectiveness of our representations on new tasks.
Furthermore, we conduct diverse ablation studies to analyze the effects of each
module.
