'Less than One'-Shot Learning: Learning N Classes from M < N Samples
Abstract
Deep neural networks require large training sets but suffer from high computational cost and long training times. Training on much smaller training sets while maintaining nearly the same accuracy would be very beneficial. In the few-shot learning setting, a model must learn a new class given only a small number of samples from that class. One-shot learning is an extreme form of few-shot learning where the model must learn a new class from a single example. We propose the 'less than one'-shot learning task where models must learn N new classes given only M
Cite
Text
Sucholutsky and Schonlau. "'Less than One'-Shot Learning: Learning N Classes from M < N Samples." AAAI Conference on Artificial Intelligence, 2021. doi:10.1609/AAAI.V35I11.17171Markdown
[Sucholutsky and Schonlau. "'Less than One'-Shot Learning: Learning N Classes from M < N Samples." AAAI Conference on Artificial Intelligence, 2021.](https://mlanthology.org/aaai/2021/sucholutsky2021aaai-less/) doi:10.1609/AAAI.V35I11.17171BibTeX
@inproceedings{sucholutsky2021aaai-less,
title = {{'Less than One'-Shot Learning: Learning N Classes from M < N Samples}},
author = {Sucholutsky, Ilia and Schonlau, Matthias},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2021},
pages = {9739-9746},
doi = {10.1609/AAAI.V35I11.17171},
url = {https://mlanthology.org/aaai/2021/sucholutsky2021aaai-less/}
}