Jul 12, 2020
Few-shot learning attempts to generalize to unlabeled query samples of new classes, which are unseen during training, given just a few labeled examples of those classes. It has received substantial research interest recently, with a large body of works based on complex meta-learning strategies and architecture choices. We propose a Laplacian-regularization objective for few-shot tasks, which integrates two types of potentials: (1) unary potentials assigning query samples to the nearest class prototype and (2) pairwise Laplacian potentials encouraging nearby query samples to have consistent predictions.We optimize a tight upper bound of a concave-convex relaxation of our objective, thereby guaranteeing convergence, while computing independent updates for each query sample. Following the standard experimental setting for few-shot learning, our LaplacianShot technique outperforms state-of-the-art methods significantly, while using simple cross-entropy training on the base classes. In the 1-shot setting on the standard miniImageNet and tieredImageNet benchmarks, and on the recent meta-iNat benchmark, across various networks, LaplacianShot consistently pro-vides 3 − 4
The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.
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