  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-1K, 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.
