Learning from limited exemplars(few shot learning) is a fundamental, unsolved problem that has been laboriously explored in the machine learning community. In the current study, we develop a method to learn an unsupervised few shot learner via self-training(UFSLS) which can effectively generalize to novel but related classes.
python3.6
pytorch1.2
Omniglot
1, put omniglot dataset in Dataset
2, python ./train/train_hardtriplet.py --hard_mining True
1, Compare with other unsupervised few shot learners
2, Clustering quality with T-SNE visualization at different training iterations
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