Few-shot Image Classification: Just Use a Library of Pre-trained Feature Extractors and a Simple Classifier
Arkabandhu Chowdhury, Mingchao Jiang, Swarat Chaudhuri, Chris Jermaine
Abstract
Recent papers have suggested that transfer learning can outperform sophisticated meta-learning methods for few-shot image classification. We take this hypothesis to its logical conclusion, and suggest the use of an ensemble of high-quality, pre-trained feature extractors for few-shot image classification. We show experimentally that a library of pre-trained feature extractors combined with a simple feed-forward network learned with an L2-regularizer can be an excellent option for solving cross-domain few-shot image classification. Our experimental results suggest that this simple approach far outperforms several well-established meta-learning algorithms.
Topics & Concepts
Computer scienceArtificial intelligencePattern recognition (psychology)Classifier (UML)Contextual image classificationFeature (linguistics)Feature extractionSimple (philosophy)Machine learningShot (pellet)Image (mathematics)Transfer of learningOne shotFeature vectorEngineeringMechanical engineeringChemistryPhilosophyLinguisticsEpistemologyOrganic chemistryDomain Adaptation and Few-Shot LearningCancer-related molecular mechanisms researchMultimodal Machine Learning Applications