Optimized Generic Feature Learning for Few-shot Classification across Domains

• URL: http://arxiv.org/abs/2001.07926v1
• Date: Wed, 22 Jan 2020 09:31:39 GMT
• Title: Optimized Generic Feature Learning for Few-shot Classification across Domains
• Authors: Tonmoy Saikia, Thomas Brox, Cordelia Schmid
• Abstract summary: We propose to use cross-domain, cross-task data as validation objective for hyper- parameter optimization (HPO) We demonstrate the effectiveness of this strategy on few-shot image classification within and across domains. The learned features outperform all previous few-shot and meta-learning approaches.
• Score: 96.4224578618561
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To learn models or features that generalize across tasks and domains is one of the grand goals of machine learning. In this paper, we propose to use cross-domain, cross-task data as validation objective for hyper-parameter optimization (HPO) to improve on this goal. Given a rich enough search space, optimization of hyper-parameters learn features that maximize validation performance and, due to the objective, generalize across tasks and domains. We demonstrate the effectiveness of this strategy on few-shot image classification within and across domains. The learned features outperform all previous few-shot and meta-learning approaches.

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