Apr 4, 2019 · Abstract:Minimization of distribution matching losses is a principled approach to domain adaptation in the context of image classification.

Minimization of distribution matching losses is a principled approach to domain adaptation in the context of image classification.

Apr 17, 2020 · We propose a simple approach allowing for efficient domain adaptation in semantic segmentation problems if source and target domain data are ...

This work proposes a class of loss functions, which encourage direct kernel density matching in the network-output space, up to some geometric ...

Source domain images X; ground truth labels Y. A segmentation function f is trained on labeled source data. L = {(Xi,Yi)}i=1,...,n.

Therefore, it simplifies segmentation adaptation by avoiding extra adversarial steps, while improving both the quality, stability and efficiency of training. We ...

Minimization of distribution matching losses is a principled approach to domain adaptation in the context of image classification.

A conceptual juxtaposition of adversarial training in the network-output space [38] (2a and 2b) and our direct kernel density matching (2c). …

On direct distribution matching for adapting segmentation networks. Pichler, Georg, Dolz, Jose, Ben Ayed, Ismail et Piantanida, Pablo. 2020. « On direct ...

On Direct Distribution Matching for Adapting Segmentation Networks. Georg ... Uncertainty-based Graph Convolutional Networks for Organ Segmentation Refinement.