10 citations · 16 across the 4 of their papers we have counts for
6 papers
Improving Generalization via Uncertainty Driven Perturbations
Matteo Pagliardini, Gilberto Manunza, Martin Jaggi +2
Recently Shah et al., 2020 pointed out the pitfalls of the simplicity bias - the tendency of gradient-based algorithms to learn simple models - which include the model's high sensi…
Semantic Perturbations with Normalizing Flows for Improved Generalization
Oguz Kaan Yuksel, Sebastian U. Stich, Martin Jaggi +1
Data augmentation is a widely adopted technique for avoiding overfitting when training deep neural networks. However, this approach requires domain-specific knowledge and is often…
Convening during COVID-19: Lessons learnt from organizing virtual workshops in 2020
Mandana Samiei, Caroline Weis, Larissa Schiavo +2
This report is an account of the authors' experiences as organizers of WiML's "Un-Workshop" event at ICML 2020. Un-workshops focus on participant-driven structured discussions on a…
Reducing Noise in GAN Training with Variance Reduced Extragradient
Tatjana Chavdarova, Gauthier Gidel, François Fleuret +1
We study the effect of the stochastic gradient noise on the training of generative adversarial networks (GANs) and show that it can prevent the convergence of standard game optimiz…
SGAN: An Alternative Training of Generative Adversarial Networks
Tatjana Chavdarova, François Fleuret
The Generative Adversarial Networks (GANs) have demonstrated impressive performance for data synthesis, and are now used in a wide range of computer vision tasks. In spite of this…
The WILDTRACK Multi-Camera Person Dataset
Tatjana Chavdarova, Pierre Baqué, Stéphane Bouquet +6
People detection methods are highly sensitive to the perpetual occlusions among the targets. As multi-camera set-ups become more frequently encountered, joint exploitation of the a…