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Simon Guiroy

guiroys@mila.quebec
PhD - Université de Montréal
Supervisor
Chris Pal
Co-supervisor
Sarath Chandar Anbil Parthipan
Website
Github
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Publications

Scaling Laws for the Few-Shot Adaptation of Pre-trained Image Classifiers
Gabriele Prato
Simon Guiroy
Ethan Caballero
Irina Rish
Sarath Chandar Anbil Parthipan
Empirical science of neural scaling laws is a rapidly growing area of significant importance to the future of machine learning, particularly… (see more) in the light of recent breakthroughs achieved by large-scale pre-trained models such as GPT-3, CLIP and DALL-e. Accurately predicting the neural network performance with increasing resources such as data, compute and model size provides a more comprehensive evaluation of different approaches across multiple scales, as opposed to traditional point-wise comparisons of fixed-size models on fixed-size benchmarks, and, most importantly, allows for focus on the best-scaling, and thus most promising in the future, approaches. In this work, we consider a challenging problem of few-shot learning in image classification, especially when the target data distribution in the few-shot phase is different from the source, training, data distribution, in a sense that it includes new image classes not encountered during training. Our current main goal is to investigate how the amount of pre-training data affects the few-shot generalization performance of standard image classifiers. Our key observations are that (1) such performance improvements are well-approximated by power laws (linear log-log plots) as the training set size increases, (2) this applies to both cases of target data coming from either the same or from a different domain (i.e., new classes) as the training data, and (3) few-shot performance on new classes converges at a faster rate than the standard classification performance on previously seen classes. Our findings shed new light on the relationship between scale and generalization.
2021-10-13
ArXiv (preprint)
openreview.net
openreview.net