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Number of items: 4.

Liang, Yuanbang ORCID: https://orcid.org/0009-0000-8370-6655, Garg, Bhavesh, Rosin, Paul ORCID: https://orcid.org/0000-0002-4965-3884 and Qin, Yipeng ORCID: https://orcid.org/0000-0002-1551-9126 2024. Deep generative model based rate-distortion for image downscaling assessment. Presented at: The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2024, Seattle, WA, USA, 17-21 June 2024. Proceedings of the Conference on Computer Vision and Pattern Recognition. IEEE, pp. 19363-19372. 10.1109/CVPR52733.2024.01832
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Liang, Yuanbang ORCID: https://orcid.org/0009-0000-8370-6655, Wu, Jing ORCID: https://orcid.org/0000-0001-5123-9861, Lai, Yukun ORCID: https://orcid.org/0000-0002-2094-5680 and Qin, Yipeng ORCID: https://orcid.org/0000-0002-1551-9126 2024. Efficient precision and recall metrics for assessing generative models using hubness-aware sampling. Presented at: The Forty-first International Conference on Machine Learning (ICML), Vienna, Austria, 21-27 July 2024. Proceedings of Machine Learning Research. , vol.235 pp. 29682-29699.
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Song, Shuang, Liang, Yuanbang ORCID: https://orcid.org/0009-0000-8370-6655, Wu, Jing ORCID: https://orcid.org/0000-0001-5123-9861, Lai, Yukun ORCID: https://orcid.org/0000-0002-2094-5680 and Qin, Yipeng ORCID: https://orcid.org/0000-0002-1551-9126 2023. Feature proliferation — the "cancer" in StyleGAN and its treatments. Presented at: International Conference on Computer Vision (ICCV) 2023, Paris, France, October 1 - 6, 2023. Proceedings of IEEE/CVF International Conference on Computer Vision. IEEE, pp. 2360-2370. 10.1109/ICCV51070.2023.00224
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Liang, Yuanbang ORCID: https://orcid.org/0009-0000-8370-6655, Wu, Jing ORCID: https://orcid.org/0000-0001-5123-9861, Lai, Yukun ORCID: https://orcid.org/0000-0002-2094-5680 and Qin, Yipeng ORCID: https://orcid.org/0000-0002-1551-9126 2022. Exploring and exploiting hubness priors for high-quality GAN latent sampling. Presented at: The 39th International Conference on Machine Learning (ICML 2022), Baltimore, Maryland USA, 17-23 July 2022. Proceedings of Machine Learning Research. , vol.162 ML Research Press, pp. 13271-13284.
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