25 citations · 26 across the 3 of their papers we have counts for
6 papers
Improving out-of-distribution generalization via multi-task self-supervised pretraining
Isabela Albuquerque, Nikhil Naik, Junnan Li +2
Self-supervised feature representations have been shown to be useful for supervised classification, few-shot learning, and adversarial robustness. We show that features obtained us…
An end-to-end approach for the verification problem: learning the right distance
Joao Monteiro, Isabela Albuquerque, Jahangir Alam +2
In this contribution, we augment the metric learning setting by introducing a parametric pseudo-distance, trained jointly with the encoder. Several interpretations are thus drawn f…
Self-supervised representation learning from electroencephalography signals
Hubert Banville, Isabela Albuquerque, Aapo Hyvärinen +3
The supervised learning paradigm is limited by the cost - and sometimes the impracticality - of data collection and labeling in multiple domains. Self-supervised learning, a paradi…
Multi-objective training of Generative Adversarial Networks with multiple discriminators
Isabela Albuquerque, João Monteiro, Thang Doan +3
Recent literature has demonstrated promising results for training Generative Adversarial Networks by employing a set of discriminators, in contrast to the traditional game involvin…
Learning to navigate image manifolds induced by generative adversarial networks for unsupervised video generation
Isabela Albuquerque, João Monteiro, Tiago H. Falk
In this work, we introduce a two-step framework for generative modeling of temporal data. Specifically, the generative adversarial networks (GANs) setting is employed to generate s…
Deep learning-based electroencephalography analysis: a systematic review
Yannick Roy, Hubert Banville, Isabela Albuquerque +3
Electroencephalography (EEG) is a complex signal and can require several years of training to be correctly interpreted. Recently, deep learning (DL) has shown great promise in help…