Enhancing Few-Shot Image Classification with Unlabelled Examples

• URL: http://arxiv.org/abs/2006.12245v6
• Date: Thu, 21 Oct 2021 17:59:05 GMT
• Title: Enhancing Few-Shot Image Classification with Unlabelled Examples
• Authors: Peyman Bateni, Jarred Barber, Jan-Willem van de Meent, Frank Wood
• Abstract summary: We develop a transductive meta-learning method that uses unlabelled instances to improve few-shot image classification performance. Our approach combines a regularized neural adaptive feature extractor to achieve improved test-time classification accuracy using unlabelled data.
• Score: 18.03136114355549
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We develop a transductive meta-learning method that uses unlabelled instances to improve few-shot image classification performance. Our approach combines a regularized Mahalanobis-distance-based soft k-means clustering procedure with a modified state of the art neural adaptive feature extractor to achieve improved test-time classification accuracy using unlabelled data. We evaluate our method on transductive few-shot learning tasks, in which the goal is to jointly predict labels for query (test) examples given a set of support (training) examples. We achieve state of the art performance on the Meta-Dataset, mini-ImageNet and tiered-ImageNet benchmarks. All trained models and code have been made publicly available at github.com/plai-group/simple-cnaps.

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