4 papers · 1 filter
NSVQ: Mitigating Codebook Collapse by Stabilizing Encoder Drift in Vector Quantization
Hao Lu, Yongxin Guo, Onur Koyun +3
Vector quantization is central to modern generative modeling pipelines, but large-codebook VQ models often suffer from codebook collapse. We identify encoder drift as a key driver…
Beyond Stationarity: Rethinking Codebook Collapse in Vector Quantization
Hao Lu, Onur C. Koyun, Yongxin Guo +3
Vector Quantization (VQ) underpins many modern generative frameworks such as VQ-VAE, VQ-GAN, and latent diffusion models. Yet, it suffers from the persistent problem of codebook co…
Adaptively Distilled ControlNet: Accelerated Training and Superior Sampling for Medical Image Synthesis
Kunpeng Qiu, Zhiying Zhou, Yongxin Guo
Medical image annotation is constrained by privacy concerns and labor-intensive labeling, significantly limiting the performance and generalization of segmentation models. While ma…
Noise-Consistent Siamese-Diffusion for Medical Image Synthesis and Segmentation
Kunpeng Qiu, Zhiqiang Gao, Zhiying Zhou +2
Deep learning has revolutionized medical image segmentation, yet its full potential remains constrained by the paucity of annotated datasets. While diffusion models have emerged as…