Portrait de Simon Guiroy

Simon Guiroy

Doctorat - UdeM
Superviseur⋅e principal⋅e
Chris Pal
Co-supervisor
Sarath Chandar
Sujets de recherche
Apprentissage de représentations
Apprentissage profond
Raisonnement
Vision par ordinateur
Site web
Google Scholar
GitHub

Publications

Improving Meta-Learning Generalization with Activation-Based Early-Stopping
Simon Guiroy
Chris Pal
Goncalo Mordido
Sarath Chandar
2022-11-28
Proceedings of The 1st Conference on Lifelong Learning Agents (publié)
doi.org
arxiv.org
Scaling Laws for the Few-Shot Adaptation of Pre-trained Image Classifiers
Gabriele Prato
Simon Guiroy
Ethan Caballero
Irina Rish
Sarath Chandar
Empirical science of neural scaling laws is a rapidly growing area of significant importance to the future of machine learning, particularly… (voir plus) 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 (prépublication)
openreview.net
openreview.net
Towards an Unsupervised Method for Model Selection in Few-Shot Learning
Simon Guiroy
Vikas Verma
Chris Pal
The study of generalization of neural networks in gradient-based meta-learning has recently great research interest. Previous work on the st… (voir plus)udy of the objective landscapes within the scope of few-shot classification empirically demonstrated that generalization to new tasks might be linked to the average inner product between their respective gradients vectors (Guiroy et al., 2019). Following that work, we study the effect that meta-training has on the learned space of representation of the network. Notably, we demonstrate that the global similarity in the space of representation, measured by the average inner product between the embeddings of meta-test examples, also correlates to generalization. Based on these observations, we propose a novel model-selection criterion for gradient-based meta-learning and experimentally validate its effectiveness.
2020-07-13
ICML.cc/2020/Workshop/LifelongML (inconnu)
openreview.net
openreview.net