![Yuanbang Liang](https://profiles-cardiff.imgix.net/attachments/7532?w=200&h=200&auto=format&crop=faces&fit=crop&q=90")
Publication
2024
- Liang, Y., Garg, B., Rosin, P. and Qin, Y. 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, USA, 17-21 June 2024.
- Liang, Y., Wu, J., Lai, Y. and Qin, Y. 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.
2023
- Song, S., Liang, Y., Wu, J., Lai, Y. and Qin, Y. 2023. Feature proliferation — the "cancer" in StyleGAN and its treatments. Presented at: International Conference on Computer Vision (ICCV) 2023, Paris, France, October 1 - 6, 2023Proceedings of IEEE/CVF International Conference on Computer Vision. IEEE pp. 2360-2370., (10.1109/ICCV51070.2023.00224)
2022
- Liang, Y., Wu, J., Lai, Y. and Qin, Y. 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.
Conferences
- Liang, Y., Garg, B., Rosin, P. and Qin, Y. 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, USA, 17-21 June 2024.
- Liang, Y., Wu, J., Lai, Y. and Qin, Y. 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.
- Song, S., Liang, Y., Wu, J., Lai, Y. and Qin, Y. 2023. Feature proliferation — the "cancer" in StyleGAN and its treatments. Presented at: International Conference on Computer Vision (ICCV) 2023, Paris, France, October 1 - 6, 2023Proceedings of IEEE/CVF International Conference on Computer Vision. IEEE pp. 2360-2370., (10.1109/ICCV51070.2023.00224)
- Liang, Y., Wu, J., Lai, Y. and Qin, Y. 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.
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.