Diversity with Cooperation: Ensemble Methods for Few-Shot Classification

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Abstract

Few-shot classification consists of learning a predictive model that is able to effectively adapt to a new class, given only a few annotated samples. To solve this challenging problem, meta-learning has become a popular paradigm that advocates the ability to ``learn to adapt''. Recent works have shown, however, that simple learning strategies without meta-learning could be competitive. In this paper, we go a step further and show that by addressing the fundamental high-variance issue of few-shot learning classifiers, it is possible to significantly outperform current meta-learning techniques. Our approach consists of designing an ensemble of deep networks to leverage the variance of the classifiers, and introducing new strategies to encourage the networks to cooperate, while encouraging prediction diversity. Evaluation is conducted on the mini-ImageNet and CUB datasets, where we show that even a single network obtained by distillation yields state-of-the-art results.

Different ways to train ensembles

Illustration of the cooperation and diversity training strategies on an ensemble consisting of two networks. All networks receive the same image as input and compute corresponding class probabilities with softmax. Cooperation encourages the non-ground truth probabilities (in red) to be similar, after normalization, thus promoting knowledge sharing between the networks and encouraging cooperation. Diversity training, on the other hand, encourages orthogonality and promotes diversity among ensemble members.

Paper

ICCV'19

BibTeX

@inproceedings{dvornik2019diversity,
              title={Diversity with Cooperation: Ensemble Methods for Few-Shot Classification},
              author={Dvornik, Nikita and Schmid, Cordelia and Mairal, Julien},
              booktitle={{IEEE International Conference on Computer Vision (ICCV)}},
              year={2019}
              }

Code

The code is available in the official GitHub repo.

Acknowledgements

This work was supported by a grant from ANR (MACARON project under grant number ANR-14-CE23-0003-01), by the ERC grant number 714381 (SOLARIS project), the ERC advanced grant ALLEGRO and gifts from Amazon and Intel.

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