Optimized Generic Feature Learning for Few-shot Classification across Domains

2020/01/22 by Saikia, Tonmoy, Brox, Thomas, Schmid, Cordelia · 2 citations
#Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences

paper · doi:10.48550/arxiv.2001.07926

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.

Cited by

• Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains
• Reliable Few-shot Learning under Dual Noises

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