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Communication Dans Un Congrès Année : 2019

Dense Classification and Implanting for Few-Shot Learning

Résumé

Training deep neural networks from few examples is a highly challenging and key problem for many computer vision tasks. In this context, we are targeting knowledge transfer from a set with abundant data to other sets with few available examples. We propose two simple and effective solutions: (i) dense classification over feature maps, which for the first time studies local activations in the domain of few-shot learning, and (ii) implanting, that is, attaching new neurons to a previously trained network to learn new, task-specific features. On miniImageNet, we improve the prior state-of-the-art on few-shot classification, i.e., we achieve 62.5%, 79.8% and 83.8% on 5-way 1-shot, 5-shot and 10-shot settings respectively.
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Dates et versions

hal-02371279 , version 1 (19-11-2019)
hal-02371279 , version 2 (21-11-2019)

Identifiants

  • HAL Id : hal-02371279 , version 1

Citer

Yann Lifchitz, Yannis Avrithis, Sylvaine Picard, Andrei Bursuc. Dense Classification and Implanting for Few-Shot Learning. CVPR 2019 - IEEE Computer Vision and Pattern Recognition Conference, Jun 2019, Long Beach, United States. ⟨hal-02371279v1⟩
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