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Fine-grained image labels are desirable for many computer visionapplications, such as visual search or mobile AI assistant. These applicationsrely on image classification models that can produce hundreds of thousands(e.g. 100K) of diversified fine-grained image labels on input images. However,training a network at this vocabulary scale is challenging, and suffers fromintolerable large model size and slow training speed, which leads tounsatisfying classification performance. A straightforward solution would betraining separate expert networks (specialists), with each specialist focusingon learning one specific vertical (e.g. cars, birds...). However, deployingdozens of expert networks in a practical system would significantly increasesystem complexity and inference latency, and consumes large amounts ofcomputational resources. To address these challenges, we propose a KnowledgeConcentration method, which effectively transfers the knowledge from dozens ofspecialists (multiple teacher networks) into one single model (one studentnetwork) to classify 100K object categories. There are three salient aspects inour method: (1) a multi-teacher single-student knowledge distillationframework; (2) a self-paced learning mechanism to allow the student to learnfrom different teachers at various paces; (3) structurally connected layers toexpand the student network capacity with limited extra parameters. We validateour method on OpenImage and a newly collected dataset, Entity-Foto-Tree (EFT),with 100K categories, and show that the proposed model performs significantlybetter than the baseline generalist model.

Knowledge Concentration: Learning 100K Object Classifiers in a Single CNN