Brain-inspired analogical mixture prototypes for few-shot class-incremental learning

TL;DR

This paper introduces BAMP, a brain-inspired method for few-shot class-incremental learning that combines prototype mixing, statistical analogy, and soft voting to improve learning efficiency and retention.

Contribution

The paper proposes a novel brain-inspired approach called BAMP that enhances FSCIL by integrating analogical reasoning with prototype learning and statistical calibration.

Findings

BAMP outperforms state-of-the-art methods on benchmark datasets.

It effectively alleviates catastrophic forgetting in FSCIL.

BAMP performs well in both big start and small start FSCIL settings.

Abstract

Few-shot class-incremental learning (FSCIL) poses significant challenges for artificial neural networks due to the need to efficiently learn from limited data while retaining knowledge of previously learned tasks. Inspired by the brain's mechanisms for categorization and analogical learning, we propose a novel approach called Brain-inspired Analogical Mixture Prototypes (BAMP). BAMP has three components: mixed prototypical feature learning, statistical analogy, and soft voting. Starting from a pre-trained Vision Transformer (ViT), mixed prototypical feature learning represents each class using a mixture of prototypes and fine-tunes these representations during the base session. The statistical analogy calibrates the mean and covariance matrix of prototypes for new classes according to similarity to the base classes, and computes classification score with Mahalanobis distance. Soft voting combines both merits of statistical analogy and an off-shelf FSCIL method. Our experiments on benchmark datasets demonstrate that BAMP outperforms state-of-the-art on both traditional big start FSCIL setting and challenging small start FSCIL setting. The study suggests that brain-inspired analogical mixture prototypes can alleviate catastrophic forgetting and over-fitting problems in FSCIL.