59 citations · 76 across the 9 of their papers we have counts for
17 papers
Dense FixMatch: a simple semi-supervised learning method for pixel-wise prediction tasks
Miquel Martí i Rabadán, Alessandro Pieropan, Hossein Azizpour +1
We propose Dense FixMatch, a simple method for online semi-supervised learning of dense and structured prediction tasks combining pseudo-labeling and consistency regularization via…
Towards Self-Supervised Learning of Global and Object-Centric Representations
Federico Baldassarre, Hossein Azizpour
Self-supervision allows learning meaningful representations of natural images, which usually contain one central object. How well does it transfer to multi-entity scenes? We discus…
Are All Linear Regions Created Equal?
Matteo Gamba, Adrian Chmielewski-Anders, Josephine Sullivan +2
The number of linear regions has been studied as a proxy of complexity for ReLU networks. However, the empirical success of network compression techniques like pruning and knowledg…
Consistency Regularization Can Improve Robustness to Label Noise
Erik Englesson, Hossein Azizpour
Consistency regularization is a commonly-used technique for semi-supervised and self-supervised learning. It is an auxiliary objective function that encourages the prediction of th…
Generalized Jensen-Shannon Divergence Loss for Learning with Noisy Labels
Erik Englesson, Hossein Azizpour
Prior works have found it beneficial to combine provably noise-robust loss functions e.g., mean absolute error (MAE) with standard categorical loss function e.g. cross entropy (CE)…
From coarse wall measurements to turbulent velocity fields through deep learning
Alejandro Güemes, Stefano Discetti, Andrea Ianiro +3
This work evaluates the applicability of super-resolution generative adversarial networks (SRGANs) as a methodology for the reconstruction of turbulent-flow quantities from coarse…