2 citations · 3 across the 6 of their papers we have counts for
6 papers
Geometric Decoupling: Diagnosing the Structural Instability of Latent
Yuanbang Liang, Zhengwen Chen, Yu-Kun Lai
Latent Diffusion Models (LDMs) achieve high-fidelity synthesis but suffer from latent space brittleness, causing discontinuous semantic jumps during editing. We introduce a Riemann…
GRADE: Probing Knowledge Gaps in LLMs through Gradient Subspace Dynamics
Yujing Wang, Yuanbang Liang, Yukun Lai +2
Detecting whether a model's internal knowledge is sufficient to correctly answer a given question is a fundamental challenge in deploying responsible LLMs. In addition to verbalisi…
Grokked Models are Better Unlearners
Yuanbang Liang, Yang Li
Grokking-delayed generalization that emerges well after a model has fit the training data-has been linked to robustness and representation quality. We ask whether this training reg…
Deep Generative Model based Rate-Distortion for Image Downscaling Assessment
Yuanbang Liang, Bhavesh Garg, Paul L Rosin +1
In this paper, we propose Image Downscaling Assessment by Rate-Distortion (IDA-RD), a novel measure to quantitatively evaluate image downscaling algorithms. In contrast to image-ba…
Feature Proliferation -- the "Cancer" in StyleGAN and its Treatments
Shuang Song, Yuanbang Liang, Jing Wu +2
Despite the success of StyleGAN in image synthesis, the images it synthesizes are not always perfect and the well-known truncation trick has become a standard post-processing techn…
Exploring and Exploiting Hubness Priors for High-Quality GAN Latent Sampling
Yuanbang Liang, Jing Wu, Yu-Kun Lai +1
Despite the extensive studies on Generative Adversarial Networks (GANs), how to reliably sample high-quality images from their latent spaces remains an under-explored topic. In thi…