Class-Incremental Learning for Honey Botanical Origin Classification with Hyperspectral Images: A Study with Continual Backpropagation

TL;DR

This paper investigates class-incremental learning for honey botanical origin classification using hyperspectral images, proposing a continual backpropagation method to enhance existing algorithms' performance in real-world scenarios.

Contribution

It introduces a novel continual backpropagation technique that improves class-incremental learning performance for hyperspectral honey classification.

Findings

CB improved CIL methods by 1-7% in accuracy

The study compares multiple CIL algorithms on real honey data

The proposed method addresses loss-of-plasticity in neural networks

Abstract

Honey is an important commodity in the global market. Honey types of different botanical origins provide diversified flavors and health benefits, thus having different market values. Developing accurate and effective botanical origin-distinguishing techniques is crucial to protect consumers' interests. However, it is impractical to collect all the varieties of honey products at once to train a model for botanical origin differentiation. Therefore, researchers developed class-incremental learning (CIL) techniques to address this challenge. This study examined and compared multiple CIL algorithms on a real-world honey hyperspectral imaging dataset. A novel technique is also proposed to improve the performance of class-incremental learning algorithms by combining with a continual backpropagation (CB) algorithm. The CB method addresses the issue of loss-of-plasticity by reinitializing a proportion of less-used hidden neurons to inject variability into neural networks. Experiments showed that CB improved the performance of most CIL methods by 1-7\%.