3 citations · 15 across the 8 of their papers we have counts for
4 papers · 1 filter
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…
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…
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…