methodology-5
Few-Shot Learning
3260 papers • 126 benchmarks • 313 datasets
Few-Shot Learning is an example of meta-learning, where a learner is trained on several related tasks, during the meta-training phase, so that it can generalize well to unseen (but related) tasks with just few examples, during the meta-testing phase. An effective approach to the Few-Shot Learning problem is to learn a common representation for various tasks and train task specific classifiers on top of this representation. Source: Penalty Method for Inversion-Free Deep Bilevel Optimization
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Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Chelsea Finn, P. Abbeel, S. Levine•Wed Mar 08 2017
An algorithm for meta-learning that is model-agnostic, in the sense that it is compatible with any model trained with gradient descent and applicable to a variety of different learning problems, including classification, regression, and reinforcement learning is proposed.
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