![Yuanbang Liang](https://profiles-cardiff.imgix.net/attachments/7532?w=200&h=200&auto=format&crop=faces&fit=crop&q=90")
Yuanbang Liang
(he/him)
Teams and roles for Yuanbang Liang
Research Associate
Research student
Publication
2024
- Liang, Y. et al. 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)
- Liang, Y. et al. 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. Vol. 235., pp.29682-29699.
2023
- Song, S. et al. 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)
2022
- Liang, Y. et al. 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. Vol. 162.ML Research Press. , pp.13271-13284.
Conferences
- Liang, Y. et al. 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)
- Liang, Y. et al. 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. Vol. 235., pp.29682-29699.
- Liang, Y. et al. 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. Vol. 162.ML Research Press. , pp.13271-13284.
- Song, S. et al. 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)
Research
My research interests are centred around machine learning and its applications in computer vision, computer graphics and content generation. My current research revolves around the hubness phenomenon to uncover the relationship between the hyper-dimensional distribution and the generative models. With my recent findings, there is a strong correlation between the manifold of the model and the sampling distribution in hyper dimension.