Embedding Propagation: Smoother Manifold for Few-Shot Classification
- URL: http://arxiv.org/abs/2003.04151v2
- Date: Mon, 13 Jul 2020 15:14:03 GMT
- Title: Embedding Propagation: Smoother Manifold for Few-Shot Classification
- Authors: Pau Rodr\'iguez, Issam Laradji, Alexandre Drouin, Alexandre Lacoste
- Abstract summary: We propose to use embedding propagation as an unsupervised non-parametric regularizer for manifold smoothing in few-shot classification.
We empirically show that embedding propagation yields a smoother embedding manifold.
We show that embedding propagation consistently improves the accuracy of the models in multiple semi-supervised learning scenarios by up to 16% points.
- Score: 131.81692677836202
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Few-shot classification is challenging because the data distribution of the
training set can be widely different to the test set as their classes are
disjoint. This distribution shift often results in poor generalization.
Manifold smoothing has been shown to address the distribution shift problem by
extending the decision boundaries and reducing the noise of the class
representations. Moreover, manifold smoothness is a key factor for
semi-supervised learning and transductive learning algorithms. In this work, we
propose to use embedding propagation as an unsupervised non-parametric
regularizer for manifold smoothing in few-shot classification. Embedding
propagation leverages interpolations between the extracted features of a neural
network based on a similarity graph. We empirically show that embedding
propagation yields a smoother embedding manifold. We also show that applying
embedding propagation to a transductive classifier achieves new
state-of-the-art results in mini-Imagenet, tiered-Imagenet, Imagenet-FS, and
CUB. Furthermore, we show that embedding propagation consistently improves the
accuracy of the models in multiple semi-supervised learning scenarios by up to
16\% points. The proposed embedding propagation operation can be easily
integrated as a non-parametric layer into a neural network. We provide the
training code and usage examples at
https://github.com/ElementAI/embedding-propagation.
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