Online Continual Learning For Visual Food Classification
By Jiangpeng He, Feng Zhu. 2021
60 citations recorded by the source corpus.
Food image classification is challenging for real-world applications since existing methods require static datasets for training and are not capable of learning from sequentially available new food images. Online continual learning aims to learn new classes from data stream by using each new data only once without forgetting the previously learned knowledge. However, none of the existing works target food image analysis, which is more difficult to learn incrementally due to its high intra-class variation with the unbalanced and unpredictable characteristics of future food-class distribution. In this paper, we address these issues by introducing (1) a novel clustering based exemplar selection algorithm to store the most representative data belonging to each learned food for knowledge replay, and (2) an effective online learning regime using balanced training batch along with the knowledge distillation on augmented exemplars to maintain the model performance on all learned classes. Our method is evaluated on a challenging large scale food image database, Food-1K1, by varying the number of newly added food classes. Our results show significant improvements compared with existing state-of-the-art online continual learning methods, showing great potential to achieve lifelong learning for food image classification in real world.
Subfields: Artificial Intelligence, Molecular Biology, Nutrition and Dietetics
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Citations in the corpus
- Orchestrate Latent Expertise: Advancing Online Continual Learning with Multi-Level Supervision and Reverse Self-Distillation (2024, background): “…size M to determine the number of old training samples that can be stored in the memory buffer; (2) memory retrieval strategy [3, 51] to gather old training samples from the memory buffer; and (3) memory update strategy [4, 23, 30] to keep an appropriate data structure across tasks and classes.”