4 papers
Efficient Fréchet Distributional Decoder Alignment for Latent Generative Models
Xianghong Fang, Jiajun Zhu, Esma Aimeur +2
Fréchet Distributional (FD) objectives provide an effective distribution-level signal for post-training generative models. For latent generative models, however, existing FD-based…
AudioGAR: Bridging Reconstruction and Generation in Latent Audio Generative Models
Xianghong Fang, Geeyang Tay, Wentao Ma +2
Latent audio generative models are typically trained in two stages: an audio codec is learned first, followed by a latent generative model. This decomposition leads to a decoder tr…
A Latent Distribution Perspective on Evaluating and Improving Latent Generative Models
Xianghong Fang, Wenjie Shu, Tongda Xu +3
In latent generative models, reconstruction quality is often assumed to correlate with generative performance. However, reconstruction FID (rFID) can exhibit weak or even negative…
A Unified Rate-Distortion Perspective on Vector, Product, and Scalar Quantization
Xianghong Fang, Wenlong Mou, Yuan Yuan +2
Discrete visual tokenization, predominantly driven by vector, scalar, and product quantization, lacks a unified conceptual framework for understanding quantization tradeoffs. In th…