GKEAL: Gaussian Kernel Embedded Analytic Learning for Few-Shot Class Incremental Task

By Huiping Zhuang, Zhenyu Weng, Run He, Zhiping Lin, Ziqian Zeng. 2023

111 citations recorded by the source corpus.

Few-shot class incremental learning (FSCIL) aims to address catastrophic forgetting during class incremental learning in a few-shot learning setting. In this paper, we approach the FSCIL by adopting analytic learning, a technique that converts network training into linear problems. This is inspired by the fact that the recursive implementation (batch-by-batch learning) of analytic learning gives identical weights to that produced by training on the entire dataset at once. The recursive implementation and the weight-identical property highly resemble the FSCIL setting (phase-by-phase learning) and its goal of avoiding catastrophic forgetting. By bridging the FSCIL with the analytic learning, we propose a Gaussian kernel embedded analytic learning (GKEAL) for FSCIL. The key components of GKEAL include the kernel analytic module which allows the GKEAL to conduct FSCIL in a recursive manner, and the augmented feature concatenation module that balances the preference between old and new tasks especially effectively under the few-shot setting. Our experiments show that the GKEAL gives state-of-the-art performance on several benchmark datasets.

Subfields: Artificial Intelligence, Cancer Research, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Han-Jia Ye
2. De-chuan Zhan
3. Da-Wei Zhou
4. Qiwen Wang
5. Ziwei Liu
6. Zhiyuan Qi
7. Hai-Long Sun
8. Wei-Lun Chao

In Cancer Research

1. De-chuan Zhan
2. Han-Jia Ye

In Computer Vision and Pattern Recognition

1. Han-Jia Ye
2. De-chuan Zhan
3. Da-Wei Zhou
4. Qiwen Wang
5. Ziwei Liu
6. Zhiyuan Qi
7. Hai-Long Sun
8. Bernt Schiele

Citations in the corpus

• Recent Advances of Continual Learning in Computer Vision: An Overview (2021, neutral): “Specifically, FGN designs a new graph topology through which the continual growth of a graph can be regarded as the continual increase of training samples, and typical convolutional-neural-network-based continual learning techniques can thus be used for graph learning.”
• Class-Incremental Learning: A Survey (2023, background): “Specifically, [27], [104], [115], [121], [216] address the few-shot CIL problem, where the model is required to be adapted to incoming few-shot classes.”
• Expandable Subspace Ensemble for Pre-Trained Model-Based Class-Incremental Learning (2024, background): “Class-Incremental Learning (CIL) : requires a learning system to continually absorb new class knowledge without forgetting existing ones [13, 14, 20, 22, 38, 57, 59, 74, 81, 82], which can be roughly divided into several categories.”