3 citations · 15 across the 8 of their papers we have counts for
8 papers
Complexity Matters: Rethinking the Latent Space for Generative Modeling
Tianyang Hu, Fei Chen, Haonan Wang +4
In generative modeling, numerous successful approaches leverage a low-dimensional latent space, e.g., Stable Diffusion models the latent space induced by an encoder and generates i…
Deciphering the Projection Head: Representation Evaluation Self-supervised Learning
Jiajun Ma, Tianyang Hu, Wenjia Wang
Self-supervised learning (SSL) aims to learn intrinsic features without labels. Despite the diverse architectures of SSL methods, the projection head always plays an important role…
An automated approach to extracting positive and negative clinical research results
Xuanyu Shi, Shiyao Xie, Wenjia Wang +2
Failure is common in clinical trials since the successful failures presented in negative results always indicate the ways that should not be taken. In this paper, we proposed an au…
Your Contrastive Learning Is Secretly Doing Stochastic Neighbor Embedding
Tianyang Hu, Zhili Liu, Fengwei Zhou +2
Contrastive learning, especially self-supervised contrastive learning (SSCL), has achieved great success in extracting powerful features from unlabeled data. In this work, we contr…
HuMMan: Multi-Modal 4D Human Dataset for Versatile Sensing and Modeling
Zhongang Cai, Daxuan Ren, Ailing Zeng +12
4D human sensing and modeling are fundamental tasks in vision and graphics with numerous applications. With the advances of new sensors and algorithms, there is an increasing deman…
Differentiable and Scalable Generative Adversarial Models for Data Imputation
Yangyang Wu, Jun Wang, Xiaoye Miao +2
Data imputation has been extensively explored to solve the missing data problem. The dramatically increasing volume of incomplete data makes the imputation models computationally i…
Neural Network Gaussian Process Considering Input Uncertainty for Composite Structures Assembly
Cheolhei Lee, Jianguo Wu, Wenjia Wang +1
Developing machine learning enabled smart manufacturing is promising for composite structures assembly process. To improve production quality and efficiency of the assembly process…
PGU-net+: Progressive Growing of U-net+ for Automated Cervical Nuclei Segmentation
Jie Zhao, Lei Dai, Mo Zhang +5
Automated cervical nucleus segmentation based on deep learning can effectively improve the quantitative analysis of cervical cancer. However, accurate nuclei segmentation is still…