55 citations · 122 across the 8 of their papers we have counts for
8 papers
HiPA: Enabling One-Step Text-to-Image Diffusion Models via High-Frequency-Promoting Adaptation
Yifan Zhang, Bryan Hooi
Diffusion models have revolutionized text-to-image generation, but their real-world applications are hampered by the extensive time needed for hundreds of diffusion steps. Although…
Expanding Small-Scale Datasets with Guided Imagination
Yifan Zhang, Daquan Zhou, Bryan Hooi +2
The power of DNNs relies heavily on the quantity and quality of training data. However, collecting and annotating data on a large scale is often expensive and time-consuming. To ad…
Reachability-Aware Laplacian Representation in Reinforcement Learning
Kaixin Wang, Kuangqi Zhou, Jiashi Feng +2
In Reinforcement Learning (RL), Laplacian Representation (LapRep) is a task-agnostic state representation that encodes the geometry of the environment. A desirable property of LapR…
The Geometry of Robust Value Functions
Kaixin Wang, Navdeep Kumar, Kuangqi Zhou +3
The space of value functions is a fundamental concept in reinforcement learning. Characterizing its geometric properties may provide insights for optimization and representation. E…
Towards Better Laplacian Representation in Reinforcement Learning with Generalized Graph Drawing
Kaixin Wang, Kuangqi Zhou, Qixin Zhang +3
The Laplacian representation recently gains increasing attention for reinforcement learning as it provides succinct and informative representation for states, by taking the eigenve…
Self-Supervised Aggregation of Diverse Experts for Test-Agnostic Long-Tailed Recognition
Yifan Zhang, Bryan Hooi, Lanqing Hong +1
Existing long-tailed recognition methods, aiming to train class-balanced models from long-tailed data, generally assume the models would be evaluated on the uniform test class dist…
Unleashing the Power of Contrastive Self-Supervised Visual Models via Contrast-Regularized Fine-Tuning
Yifan Zhang, Bryan Hooi, Dapeng Hu +2
Contrastive self-supervised learning (CSL) has attracted increasing attention for model pre-training via unlabeled data. The resulted CSL models provide instance-discriminative vis…
Understanding and Resolving Performance Degradation in Graph Convolutional Networks
Kuangqi Zhou, Yanfei Dong, Kaixin Wang +4
A Graph Convolutional Network (GCN) stacks several layers and in each layer performs a PROPagation operation (PROP) and a TRANsformation operation (TRAN) for learning node represen…