Optimized Generic Feature Learning for Few-shot Classification across Domains
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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BIG-bench Machine LearningClassificationFew-Shot Image ClassificationGeneral Classificationimage-classificationImage ClassificationMeta-LearningSimilar Papers 제목 키워드 기반
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