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Visual food recognition on mobile devices has attracted increasing attention in recent years due to its roles in individual diet monitoring and social health management and analysis. Existing visual food recognition approaches usually use large server-based networks to achieve high accuracy. However, these networks are not compact enough to be deployed on mobile devices. Even though some compact architectures have been proposed, most of them are unable to obtain the performance of full-size networks. In view of this, this paper proposes a Joint-learning Distilled Network (JDNet) that targets to achieve a high food recognition accuracy of a compact student network by learning from a large teacher network, while retaining a compact network size. Compared to the conventional one-directional knowledge distillation methods, the proposed JDNet has a novel joint-learning framework where the large teacher network and the small student network are trained simultaneously, by leveraging on different intermediate layer features in both network. JDNet introduces a new Multi-Stage Knowledge Distillation (MSKD) for simultaneous student-teacher training at different levels of abstraction. A new Instance Activation Learning (IAL) is also proposed to jointly train student and teacher on instance activation map of each training sample. Experimental results show that the trained student model is able to achieve a state-of-the-art Top-1 recognition accuracy on the benchmark UECFood-256 and Food-101 datasets at 84.0% and 91.2%, respectively, and retaining a 4x smaller network size for mobile deployment.
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