Semi-Supervised Few-Shot Class-Incremental Learning

• Published in: 2021 IEEE International Conference on Image Processing (ICIP) pp. 1239 - 1243
• Main Authors: Cui, Yawen, Xiong, Wuti, Tavakolian, Mohammad, Liu, Li
• Format: Conference Proceeding
• Language: English
• Published: IEEE 19.09.2021
• Subjects: Benchmark testing; Conferences; Few-shot learning; Human intelligence; Image classification; Image processing; incremental learning; semi-supervised learning; Training
• ISSN: 2381-8549
• DOI: 10.1109/ICIP42928.2021.9506346

The capability of incrementally learning new classes and learning from a few examples is one of the hallmarks of human intelligence. It is crucial to endow a practical recognition system with such ability. Therefore, in this paper, we conduct pioneering work and focus on a challenging yet practical Semi-Supervised Few-Shot Class-Incremental Learning (SSFSCIL) problem, which requires CNN models incrementally learn new classes from very few labeled samples and a large number of unlabeled samples, without forgetting the previously learned ones. To address this problem, a simple and efficient solution for SSFSCIL is proposed to learn novel categories using a self-training strategy in a semi-supervised manner and avoid catastrophic forgetting by distillation-based methods. Our extensive experiments on CIFAR100, mini ImageNet and CUB200 datasets demonstrate the promising performance of our proposed method, and define baselines in this new research direction.