5 papers
Normalizing Trajectory Models
Jiatao Gu, Tianrong Chen, Ying Shen +3
Diffusion-based models decompose sampling into many small Gaussian denoising steps -- an assumption that breaks down when generation is compressed to a few coarse transitions. Exis…
STARFlow2: Bridging Language Models and Normalizing Flows for Unified Multimodal Generation
Ying Shen, Tianrong Chen, Yuan Gao +6
Deep generative models have advanced rapidly across text and vision, motivating unified multimodal systems that can understand, reason over, and generate interleaved text-image seq…
WhispEar: A Bi-directional Framework for Scaling Whispered Speech Conversion via Pseudo-Parallel Whisper Generation
Zihao Fang, Yingda Shen, Zifan Guan +3
Whispered speech lacks vocal fold vibration and fundamental frequency, resulting in degraded acoustic cues and making whisper-to-normal (W2N) conversion challenging, especially wit…
STARFlow-V: End-to-End Video Generative Modeling with Normalizing Flows
Jiatao Gu, Ying Shen, Tianrong Chen +6
Normalizing flows (NFs) are end-to-end likelihood-based generative models for continuous data, and have recently regained attention with encouraging progress on image generation. Y…
Kaleido Diffusion: Improving Conditional Diffusion Models with Autoregressive Latent Modeling
Jiatao Gu, Ying Shen, Shuangfei Zhai +3
Diffusion models have emerged as a powerful tool for generating high-quality images from textual descriptions. Despite their successes, these models often exhibit limited diversity…