1 citations · 2 across the 8 of their papers we have counts for
10 papers
PA1-OPD: One-Step Prefix-Augmented On-Policy Distillation for Language Model Pretraining
Zhenhe Wu, Jian Yang, Yaping Jin +3
Knowledge distillation (KD) improves language-model pretraining by matching teacher distributions at corpus prefixes, but provides no direct supervision at contexts induced by stud…
HybridSB-MoE: Dual-Domain Schrödinger Bridges with Scene-Adaptive Expert Routing for Speech Enhancement
Zhengyi Lu, Aswini Sivakumar, Jie Hu +1
Generative speech enhancement faces three gaps: spectral models capture harmonic structure but often disrupt phase, waveform models preserve phase but miss harmonics, and Schröding…
SaD: A Scenario-Aware Discriminator for Speech Enhancement
Xihao Yuan, Siqi Liu, Yan Chen +4
Generative adversarial network-based models have shown remarkable performance in the field of speech enhancement. However, the current optimization strategies for these models pred…
EAM: Enhancing Anything with Diffusion Transformers for Blind Super-Resolution
Haizhen Xie, Kunpeng Du, Qiangyu Yan +5
Utilizing pre-trained Text-to-Image (T2I) diffusion models to guide Blind Super-Resolution (BSR) has become a predominant approach in the field. While T2I models have traditionally…
Dynamic Frequency-Adaptive Knowledge Distillation for Speech Enhancement
Xihao Yuan, Siqi Liu, Hanting Chen +3
Deep learning-based speech enhancement (SE) models have recently outperformed traditional techniques, yet their deployment on resource-constrained devices remains challenging due t…
Learning Quantized Adaptive Conditions for Diffusion Models
Yuchen Liang, Yuchuan Tian, Lei Yu +4
The curvature of ODE trajectories in diffusion models hinders their ability to generate high-quality images in a few number of function evaluations (NFE). In this paper, we propose…
One Step Diffusion-based Super-Resolution with Time-Aware Distillation
Xiao He, Huaao Tang, Zhijun Tu +8
Diffusion-based image super-resolution (SR) methods have shown promise in reconstructing high-resolution images with fine details from low-resolution counterparts. However, these a…
Multi-Granularity Semantic Revision for Large Language Model Distillation
Xiaoyu Liu, Yun Zhang, Wei Li +7
Knowledge distillation plays a key role in compressing the Large Language Models (LLMs), which boosts a small-size student model under large teacher models' guidance. However, exis…
Instruct-IPT: All-in-One Image Processing Transformer via Weight Modulation
Yuchuan Tian, Jianhong Han, Hanting Chen +5
Due to the unaffordable size and intensive computation costs of low-level vision models, All-in-One models that are designed to address a handful of low-level vision tasks simultan…
U-DiTs: Downsample Tokens in U-Shaped Diffusion Transformers
Yuchuan Tian, Zhijun Tu, Hanting Chen +3
Diffusion Transformers (DiTs) introduce the transformer architecture to diffusion tasks for latent-space image generation. With an isotropic architecture that chains a series of tr…