Continual Learning for Food Category Classification Dataset: Enhancing Model Adaptability and Performance
This addresses the issue of limited adaptability in food classification for applications like dietary monitoring, though it appears incremental.
The paper tackles the problem of recognizing new food categories not in the original training set by proposing a continual learning framework for text-guided food classification, enabling incremental updates without degrading prior knowledge.
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.