Abstract
| Language | English |
|---|---|
| Title of host publication | 2019 IEEE Winter Conference on Applications of Computer Vision (WACV) |
| Place of Publication | Piscataway |
| Publisher | Institute of Electrical and Electronics Engineers |
| Pages | 1866-1875 |
| Number of pages | 10 |
| ISBN (Electronic) | 978-1-7281-1975-5 |
| DOIs | |
| State | Published - 2019 |
| Event | 2019 IEEE Winter Conference on Applications of Computer Vision (WACV) - Waikoloa Village, United States Duration: 7 Jan 2019 → 11 Jan 2019 |
Conference
| Conference | 2019 IEEE Winter Conference on Applications of Computer Vision (WACV) |
|---|---|
| Country | United States |
| City | Waikoloa Village |
| Period | 7/01/19 → 11/01/19 |
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A domain agnostic normalization layer for unsupervised adversarial domain adaptation. / Romijnders, R.R.F.M.; Meletis, Panagiotis; Dubbelman, Gijs.
2019 IEEE Winter Conference on Applications of Computer Vision (WACV). Piscataway : Institute of Electrical and Electronics Engineers, 2019. p. 1866-1875.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review
TY - GEN
T1 - A domain agnostic normalization layer for unsupervised adversarial domain adaptation
AU - Romijnders,R.R.F.M.
AU - Meletis,Panagiotis
AU - Dubbelman,Gijs
PY - 2019
Y1 - 2019
N2 - We propose a normalization layer for unsupervised domain adaption in semantic scene segmentation. Normalization layers are known to improve convergence and generalization and are part of many state-of-the-art fully-convolutional neural networks. We show that conventional normalization layers worsen the performance of current Unsupervised Adversarial Domain Adaption (UADA), which is a method to improve network performance on unlabeled data sets and the focus of our research. Therefore, we propose a novel Domain Agnostic Normalization layer and thereby unlock the benefits of normalization layers for unsupervised adversarial domain adaptation. In our evaluation, we adapt from the synthetic GTA5 data set to the real Cityscapes data set, a common benchmark experiment, and surpass the state-of-the-art. As our normalization layer is domain agnostic at test time, we furthermore demonstrate that UADA using Domain Agnostic Normalization improves performance on unseen domains, specifically on Apolloscape and Mapillary.
AB - We propose a normalization layer for unsupervised domain adaption in semantic scene segmentation. Normalization layers are known to improve convergence and generalization and are part of many state-of-the-art fully-convolutional neural networks. We show that conventional normalization layers worsen the performance of current Unsupervised Adversarial Domain Adaption (UADA), which is a method to improve network performance on unlabeled data sets and the focus of our research. Therefore, we propose a novel Domain Agnostic Normalization layer and thereby unlock the benefits of normalization layers for unsupervised adversarial domain adaptation. In our evaluation, we adapt from the synthetic GTA5 data set to the real Cityscapes data set, a common benchmark experiment, and surpass the state-of-the-art. As our normalization layer is domain agnostic at test time, we furthermore demonstrate that UADA using Domain Agnostic Normalization improves performance on unseen domains, specifically on Apolloscape and Mapillary.
U2 - 10.1109/WACV.2019.00203
DO - 10.1109/WACV.2019.00203
M3 - Conference contribution
SP - 1866
EP - 1875
BT - 2019 IEEE Winter Conference on Applications of Computer Vision (WACV)
PB - Institute of Electrical and Electronics Engineers
CY - Piscataway
ER -