activity
20172020
most citedMulti-objective training of Generative Adversarial Networks with multiple discriminators

25 citations · 25 across the 2 of their papers we have counts for

collaborators

7 papers

eess.SP2020

TILES-2018, a longitudinal physiologic and behavioral data set of hospital workers

Karel Mundnich, Brandon M. Booth, Michelle L'Hommedieu +11

We present a novel longitudinal multimodal corpus of physiological and behavioral data collected from direct clinical providers in a hospital workplace. We designed the study to in…

cs.LG2020

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…

cs.LG201925 cited

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…

cs.CV2019

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…

cs.LG2019

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…

cs.CV2018

Generalizable Adversarial Examples Detection Based on Bi-model Decision Mismatch

João Monteiro, Isabela Albuquerque, Zahid Akhtar +1

Modern applications of artificial neural networks have yielded remarkable performance gains in a wide range of tasks. However, recent studies have discovered that such modelling st…