[IPOL announce] new article: A Study of RobustNet, a Domain Generalization Method for Semantic Segmentation
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Mon Oct 31 20:08:22 CET 2022
A new article is available in IPOL: http://www.ipol.im/pub/art/2022/433/
Xavier Bou,
A Study of RobustNet, a Domain Generalization Method for Semantic
Segmentation,
Image Processing On Line, 12 (2022), pp. 469–479.
https://doi.org/10.5201/ipol.2022.433
Abstract
Domain Generalization alleviates the domain gap between training set and
test set, improving the performance of deep neural networks on
out-of-dataset data. This opens the possibility of deploying models on
unlabelled data that were previously pretrained on other datasets. In
this article, we study the ideas and performance of RobustNet [Choi et
al. CVPR 2021], a recent method for Domain Generalization in Urban-Scene
Semantic Segmentation. Instead of exposing the network to a wide range
of domains, RobustNet tries to separate domain-variant from
domain-invariant features via a whitening transformation. Then, only the
domain invariant features are used for training, which allows to reduce
training time since no combination of datasets is needed to achieve
domain invariance. In addition, we provide an easy-to-use demo where
users can quickly test their own data and compare the results of
RobustNet against the state of the art for semantic segmentation.
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