A Universal Representation Transformer Layer for Few-Shot Image Classification

• URL: http://arxiv.org/abs/2006.11702v4
• Date: Wed, 2 Sep 2020 22:35:10 GMT
• Title: A Universal Representation Transformer Layer for Few-Shot Image Classification
• Authors: Lu Liu, William Hamilton, Guodong Long, Jing Jiang, Hugo Larochelle
• Abstract summary: Few-shot classification aims to recognize unseen classes when presented with only a small number of samples. We consider the problem of multi-domain few-shot image classification, where unseen classes and examples come from diverse data sources. Here, we propose a Universal Representation Transformer layer, that meta-learns to leverage universal features for few-shot classification.
• Score: 43.31379752656756
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Few-shot classification aims to recognize unseen classes when presented with only a small number of samples. We consider the problem of multi-domain few-shot image classification, where unseen classes and examples come from diverse data sources. This problem has seen growing interest and has inspired the development of benchmarks such as Meta-Dataset. A key challenge in this multi-domain setting is to effectively integrate the feature representations from the diverse set of training domains. Here, we propose a Universal Representation Transformer (URT) layer, that meta-learns to leverage universal features for few-shot classification by dynamically re-weighting and composing the most appropriate domain-specific representations. In experiments, we show that URT sets a new state-of-the-art result on Meta-Dataset. Specifically, it achieves top-performance on the highest number of data sources compared to competing methods. We analyze variants of URT and present a visualization of the attention score heatmaps that sheds light on how the model performs cross-domain generalization. Our code is available at https://github.com/liulu112601/URT.

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