Search Results for author: João Monteiro

Found 6 papers, 3 papers with code

Generalizing to unseen domains via distribution matching

1 code implementation3 Nov 2019 Isabela Albuquerque, João Monteiro, Mohammad Darvishi, Tiago H. Falk, Ioannis Mitliagkas

In this work, we tackle such problem by focusing on domain generalization: a formalization where the data generating process at test time may yield samples from never-before-seen domains (distributions).

Domain Generalization Object Recognition +2

Cross-Subject Statistical Shift Estimation for Generalized Electroencephalography-based Mental Workload Assessment

no code implementations20 Jun 2019 Isabela Albuquerque, João Monteiro, Olivier Rosanne, Abhishek Tiwari, Jean-François Gagnon, Tiago H. Falk

Besides shedding light on the assumptions that hold for a particular dataset, the estimates of statistical shifts obtained with the proposed approach can be used for investigating other aspects of a machine learning pipeline, such as quantitatively assessing the effectiveness of domain adaptation strategies.

Domain Adaptation EEG

Multi-objective training of Generative Adversarial Networks with multiple discriminators

1 code implementation ICLR 2019 Isabela Albuquerque, João Monteiro, Thang Doan, Breandan Considine, Tiago Falk, Ioannis Mitliagkas

Recent literature has demonstrated promising results for training Generative Adversarial Networks by employing a set of discriminators, in contrast to the traditional game involving one generator against a single adversary.

Learning to navigate image manifolds induced by generative adversarial networks for unsupervised video generation

1 code implementation23 Jan 2019 Isabela Albuquerque, João Monteiro, Tiago H. Falk

Afterwards, a recurrent model is trained with the goal of providing a sequence of inputs to the previously trained frames generator, thus yielding scenes which look natural.

Video Generation

Generalizable Adversarial Examples Detection Based on Bi-model Decision Mismatch

no code implementations21 Feb 2018 João Monteiro, Isabela Albuquerque, Zahid Akhtar, Tiago H. Falk

Non-linear binary classifiers trained on top of our proposed features can achieve a high detection rate (>90%) in a set of white-box attacks and maintain such performance when tested against unseen attacks.

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