Continual Learning for Food Category Classification Dataset: Enhancing Model Adaptability and Performance

TL;DR

This paper introduces a continual learning framework for text-guided food classification that allows models to incrementally learn new food categories without forgetting previous ones, improving adaptability in dietary monitoring applications.

Contribution

The paper presents a novel continual learning approach specifically designed for food classification, enabling incremental updates without retraining from scratch.

Findings

Model can learn new food categories without degrading existing knowledge

Framework demonstrates potential for adaptive food recognition in real-world applications

Initial results show promise for dietary monitoring and personalized nutrition

Abstract

Conventional machine learning pipelines often struggle to recognize categories absent from the original trainingset. This gap typically reduces accuracy, as fixed datasets rarely capture the full diversity of a domain. To address this, we propose a continual learning framework for text-guided food classification. Unlike approaches that require retraining from scratch, our method enables incremental updates, allowing new categories to be integrated without degrading prior knowledge. For example, a model trained on Western cuisines could later learn to classify dishes such as dosa or kimchi. Although further refinements are needed, this design shows promise for adaptive food recognition, with applications in dietary monitoring and personalized nutrition planning.