Learning a Few-shot Embedding Model with Contrastive Learning.
2021
Few-shot learning (FSL) aims to recognize target classes by adapting the prior knowledge learned from source classes.
Such knowledge usually resides in a deep embedding model for a general matching purpose of the support and query image pairs.
The objective of this paper is to repurpose the contrastive learning for such matching to learn a few-shot embedding model.
We make the following contributions:
(i) We investigate the contrastive learning with Noise Contrastive Estimation (NCE) in a supervised manner for training a few-shot embedding model;
(ii) We propose a novel contrastive training scheme dubbed infoPatch, exploiting the patch-wise relationship to substantially improve the popular infoNCE;
(iii) We show that the embedding learned by the proposed infoPatch is more effective;
(iv) Our model is thoroughly evaluated on few-shot recognition task; and demonstrates state-of-the-art results on miniImageNet and appealing performance on tieredImageNet, Fewshot-CIFAR100 (FC-100).
- Correction
- Source
- Cite
- Save
- Machine Reading By IdeaReader
34
References
7
Citations
NaN
KQI