Preprint - https://arxiv.org/abs/2009.08449

Published - In AAAI 2021 Proceedings

Code and appendix - Paper1 directory

TL;DR - Explore the decision landscapes generated by soft-label k-Nearest Neighbors classifiers in the 'less than one'-shot learning setting.

Press coverage - LO-Shot Learning has received significant press coverage.

Online demo -

Preprint - https://arxiv.org/abs/2011.00228

Published - In PeerJ Computer Science

Code - Paper2 directory

TL;DR - Design optimal 1-NN prototypes even in pathological cases where most prototype methods fail.

Preprint - https://arxiv.org/abs/2102.07834

Published - In IJCNN 2021 Proceedings

Code - Paper3 directory

TL;DR - Represent your training dataset with fewer prototypes than even the number of classes found in the data.

Preprint - https://arxiv.org/abs/2202.04670

Published - In CogSci 2022 Proceedings

Code - LOSLP directory

TL;DR - Humans can also do less-than-one-shot learning.

Preprint - https://arxiv.org/abs/1910.02551v3

Code - https://github.com/ilia10000/dataset-distillation

TL;DR - Experiments with soft-label dataset distillation (an algorithm for generating small synthetic datasets that train neural networks to the same performance as when training on the original data) provided the first evidence of LO-Shot Learning in neural networks.