Exemplar-Free Class Incremental Learning via Incremental Representation
Libo Huang Zhulin An Yan Zeng Chuanguang Yang Xinqiang Yu Yongjun Xu
Exemplar-Free Class Incremental Learning (efCIL) aims to continuously
incorporate the knowledge from new classes while retaining previously learned
information, without storing any old-class exemplars (i.e., samples). For this
purpose, various efCIL methods have been proposed over the past few years,
generally with elaborately constructed old pseudo-features, increasing the
difficulty of model development and interpretation. In contrast, we propose a
\textbf{simple Incremental Representation (IR) framework} for efCIL without
constructing old pseudo-features. IR utilizes dataset augmentation to cover a
suitable feature space and prevents the model from forgetting by using a single
L2 space maintenance loss. We discard the transient classifier trained on each
one of the sequence tasks and instead replace it with a 1-near-neighbor
classifier for inference, ensuring the representation is incrementally updated
during CIL. Extensive experiments demonstrate that our proposed IR achieves
comparable performance while significantly preventing the model from forgetting
on CIFAR100, TinyImageNet, and ImageNetSubset datasets.