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COMPAS: Representation Learning with Compositional Part Sharing for Few-Shot Classification
Few-shot image classification consists of two consecutive learning processes:
1) In the meta-learning stage, the model acquires a knowledge base from a set
of training classes. 2) During meta-testing, the acquired knowledge is used to
recognize unseen classes from very few examples. Inspired by the compositional
representation of objects in humans, we train a neural network architecture
that explicitly represents objects as a set of parts and their spatial
composition. In particular, during meta-learning, we train a knowledge base
that consists of a dictionary of part representations and a dictionary of part
activation maps that encode common spatial activation patterns of parts. The
elements of both dictionaries are shared among the training classes. During
meta-testing, the representation of unseen classes is learned using the part
representations and the part activation maps from the knowledge base. Finally,
an attention mechanism is used to strengthen those parts that are most
important for each category. We demonstrate the value of our compositional
learning framework for a few-shot classification using miniImageNet,
tieredImageNet, CIFAR-FS, and FC100, where we achieve state-of-the-art
performance
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