Transductive Information Maximization For Few-Shot Learning

• URL: http://arxiv.org/abs/2008.11297v3
• Date: Fri, 23 Oct 2020 17:36:19 GMT
• Title: Transductive Information Maximization For Few-Shot Learning
• Authors: Malik Boudiaf, Ziko Imtiaz Masud, J\'er\^ome Rony, Jos\'e Dolz, Pablo Piantanida, Ismail Ben Ayed
• Abstract summary: We introduce Transductive Infomation Maximization (TIM) for few-shot learning. Our method maximizes the mutual information between the query features and their label predictions for a given few-shot task. We propose a new alternating-direction solver for our mutual-information loss.
• Score: 41.461586994394565
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce Transductive Infomation Maximization (TIM) for few-shot learning. Our method maximizes the mutual information between the query features and their label predictions for a given few-shot task, in conjunction with a supervision loss based on the support set. Furthermore, we propose a new alternating-direction solver for our mutual-information loss, which substantially speeds up transductive-inference convergence over gradient-based optimization, while yielding similar accuracy. TIM inference is modular: it can be used on top of any base-training feature extractor. Following standard transductive few-shot settings, our comprehensive experiments demonstrate that TIM outperforms state-of-the-art methods significantly across various datasets and networks, while used on top of a fixed feature extractor trained with simple cross-entropy on the base classes, without resorting to complex meta-learning schemes. It consistently brings between 2% and 5% improvement in accuracy over the best performing method, not only on all the well-established few-shot benchmarks but also on more challenging scenarios,with domain shifts and larger numbers of classes.

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