The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
The Contextual Loss for Image Transformation with Non-aligned Data
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Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Unpaired image-to-image translation using cycle-consistent adversarial networks
Zhu, J.-Y., Park, T., Isola, P., & Efros, A. A. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.244 · Cited by 23,602
Image-to-Image Translation with Conditional Adversarial Networks
Isola, P., Zhu, J.-Y., Zhou, T., & Efros, A. A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.632 · Cited by 22,930
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Ledig, C., Theis, L., Huszar, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., et al. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.19 · Cited by 12,696
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., et al. (2016) · arXiv · doi:10.48550/arxiv.1603.04467 · Cited by 9,408
Learning to Discover Cross-Domain Relations with Generative Adversarial Networks
Kim, T., Cha, M., Kim, H., Lee, J. K., & Kim, J. (2017) · arXiv · doi:10.48550/arxiv.1703.05192 · Cited by 1,716
Photographic Image Synthesis with Cascaded Refinement Networks
Chen, Q., & Koltun, V. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.168 · Cited by 887
Cited by
Sakaridis, C., Dai, D., & Van Gool, L. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00747 · Cited by 281
Rethinking Lossy Compression: The Rate-Distortion-Perception Tradeoff
Blau, Y., & Michaeli, T. (2019) · arXiv · doi:10.48550/arxiv.1901.07821 · Cited by 198
Splicing ViT Features for Semantic Appearance Transfer
Tumanyan, N., Bar-Tal, O., Bagon, S., & Dekel, T. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01048 · Cited by 153
Multimodal Style Transfer via Graph Cuts
Zhang, Y., Fang, C., Wang, Y., Wang, Z., Lin, Z., Fu, Y., & Yang, J. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00604 · Cited by 98
ICface: Interpretable and Controllable Face Reenactment Using GANs
Tripathy, S., Kannala, J., & Rahtu, E. (2020) · 2020 IEEE Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv45572.2020.9093474 · Cited by 88
Metadata
- Authors
- Mechrez, R., Talmi, I., & Zelnik-Manor, L.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2018
- Pages
- 800-815
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2792410992
- Cited by
- 411
- References
- 7
- FWCI
- 14.38
- Type
- conference paper
- Language
- en
- Open access
- green
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- CoChat. (2026). Citation check: The Contextual Loss for Image Transformation with Non-aligned Data. https://app.cochat.ai/papers/10.1007/978-3-030-01264-9_47
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- Mechrez, R., Talmi, I., & Zelnik-Manor, L. (2018). The Contextual Loss for Image Transformation with Non-aligned Data. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-030-01264-9_47
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
