research
∙
08/26/2021
Re-using Adversarial Mask Discriminators for Test-time Training under Distribution Shifts
Thanks to their ability to learn flexible data-driven losses, Generative...
share
research
∙
08/26/2021
Stop Throwing Away Discriminators! Re-using Adversaries for Test-Time Training
Thanks to their ability to learn data distributions without requiring pa...
research
∙
08/26/2021
Self-supervised Multi-scale Consistency for Weakly Supervised Segmentation Learning
Collecting large-scale medical datasets with fine-grained annotations is...
research
∙
07/02/2020
Weakly Supervised Segmentation with Multi-scale Adversarial Attention Gates
Large, fine-grained image segmentation datasets, annotated at pixel-leve...
research
∙
08/29/2019
Temporal Consistency Objectives Regularize the Learning of Disentangled Representations
There has been an increasing focus in learning interpretable feature rep...
research
∙
10/29/2018
Unsupervised Data Selection for Supervised Learning
Recent research put a big effort in the development of deep learning arc...
research
∙
10/25/2018
Training of a Skull-Stripping Neural Network with efficient data augmentation
Skull-stripping methods aim to remove the non-brain tissue from acquisit...
research
∙
07/02/2018