Adaptive Subspaces for Few-Shot Learning

2020-06-01 · CVPR 2020 6 · Christian Simon, Piotr Koniusz, Richard Nock, Mehrtash Harandi

Object recognition requires a generalization capability to avoid overfitting, especially when the samples are extremely few. Generalization from limited samples, usually studied under the umbrella of meta-learning, equips learning techniques with the ability to adapt quickly in dynamical environments and proves to be an essential aspect of life long learning. In this paper, we provide a framework for few-shot learning by introducing dynamic classifiers that are constructed from few samples. A subspace method is exploited as the central block of a dynamic classifier. We will empirically show that such modelling leads to robustness against perturbations (e.g., outliers) and yields competitive results on the task of supervised and semi-supervised few-shot classification. We also develop a discriminative form which can boost the accuracy even further. Our code is available at https://github.com/chrysts/dsn_fewshot

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Code (1)

chrysts/dsn_fewshot 공식 구현 pytorch

Tasks

Few-Shot Image Classification Few-Shot Learning Meta-Learning Object Recognition

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class-incremental learning Class Incremental Learning Few-Shot Class-Incremental Learning image-classification+2