Papers related to 'Less Than One'-Shot (LO-Shot) Learning

Papers found in this repo

Paper 1 - 'Less Than One'-Shot Learning: Learn N Classes from M<N Samples

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 -

Paper 2 - Optimal 1-NN Prototypes for Pathological Geometries

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.

Paper 3 - One Line to Rule Them All: Generating LO-Shot Soft-Label Prototypes

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.

Paper 4 - Can humans do less-than-one-shot learning?

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

Published - In CogSci 2022 Proceedings

Code - LOSLP directory

TL;DR - Humans can also do LO-shot learning.

Paper 5 - Using Compositionality to Learn Many Categories from Few Examples

Preprint - https://osf.io/preprints/psyarxiv/upn8e

Published - In CogSci 2024 Proceedings

Code - TBA

TL;DR - Humans are better at LO-shot learning when using techniques like compositional generalization.

Papers found in other repos

Paper - Soft-Label Dataset Distillation and Text Dataset Distillation

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.