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Learning to Compare: Relation Network for Few-Shot Learning
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TLDR
Relation Network (RN) as mentioned in this paper learns to learn a deep distance metric to compare a small number of images within episodes, each of which is designed to simulate the few-shot setting.Abstract:
We present a conceptually simple, flexible, and general framework for few-shot learning, where a classifier must learn to recognise new classes given only few examples from each. Our method, called the Relation Network (RN), is trained end-to-end from scratch. During meta-learning, it learns to learn a deep distance metric to compare a small number of images within episodes, each of which is designed to simulate the few-shot setting. Once trained, a RN is able to classify images of new classes by computing relation scores between query images and the few examples of each new class without further updating the network. Besides providing improved performance on few-shot learning, our framework is easily extended to zero-shot learning. Extensive experiments on five benchmarks demonstrate that our simple approach provides a unified and effective approach for both of these two tasks.read more
Citations
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Journal ArticleDOI
Generalizing from a Few Examples: A Survey on Few-shot Learning
TL;DR: A thorough survey to fully understand Few-shot Learning (FSL), and categorizes FSL methods from three perspectives: data, which uses prior knowledge to augment the supervised experience; model, which used to reduce the size of the hypothesis space; and algorithm, which using prior knowledgeto alter the search for the best hypothesis in the given hypothesis space.
Proceedings ArticleDOI
Meta-Learning With Differentiable Convex Optimization
TL;DR: The objective is to learn feature embeddings that generalize well under a linear classification rule for novel categories and this work exploits two properties of linear classifiers: implicit differentiation of the optimality conditions of the convex problem and the dual formulation of the optimization problem.
Proceedings Article
TADAM: Task dependent adaptive metric for improved few-shot learning
TL;DR: This work identifies that metric scaling and metric task conditioning are important to improve the performance of few-shot algorithms and proposes and empirically test a practical end-to-end optimization procedure based on auxiliary task co-training to learn a task-dependent metric space.
Posted Content
Generalizing from a Few Examples: A Survey on Few-Shot Learning
TL;DR: A thorough survey to fully understand Few-Shot Learning (FSL), and categorizes FSL methods from three perspectives: data, which uses prior knowledge to augment the supervised experience; model, which used to reduce the size of the hypothesis space; and algorithm, which using prior knowledgeto alter the search for the best hypothesis in the given hypothesis space.
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Meta-Learning in Neural Networks: A Survey
TL;DR: A new taxonomy is proposed that provides a more comprehensive breakdown of the space of meta-learning methods today, including few-shot learning, reinforcement learning and architecture search, and promising applications and successes.
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