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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...
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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...
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08/26/2021
Self-supervised Multi-scale Consistency for Weakly Supervised Segmentation Learning
Collecting large-scale medical datasets with fine-grained annotations is...
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08/27/2020
Metrics for Exposing the Biases of Content-Style Disentanglement
Recent state-of-the-art semi- and un-supervised solutions for challengin...
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07/02/2020
Weakly Supervised Segmentation with Multi-scale Adversarial Attention Gates
Large, fine-grained image segmentation datasets, annotated at pixel-leve...
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08/29/2019
Temporal Consistency Objectives Regularize the Learning of Disentangled Representations
There has been an increasing focus in learning interpretable feature rep...
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10/29/2018
Unsupervised Data Selection for Supervised Learning
Recent research put a big effort in the development of deep learning arc...
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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...
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09/11/2018
Convolutional Neural Networks for the segmentation of microcalcification in Mammography Imaging
Cluster of microcalcifications can be an early sign of breast cancer. In...
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07/02/2018