8 citations · 8 across the 8 of their papers we have counts for
8 papers
P2Flow: Phoneme-aware Progressive Flow Matching for Extreme Speech Super-Resolution
Ningyuan Yang, Yize Li, Pu Zhao +5
Generative models have recently demonstrated considerable promise in speech super-resolution (SSR). Nevertheless, the majority of existing work has concentrated on standard or vers…
MRMAD: A Multi-Round Multi-Audio Benchmark for Evaluating Acoustic Degradation Perception in Large Audio-Language Models
Yize Li, Ningyuan Yang, Sile Yin +6
Large audio-language models (LALMs) have shown promising progress in understanding speech, music, and general sound events, yet their ability to reason about how audio signals are…
A Survey of Advancing Audio Super-Resolution and Bandwidth Extension from Discriminative to Generative Models
Ningyuan Yang, Yize Li, Diego A. Cuji +4
Audio super-resolution (SR), also referred to as bandwidth extension (BWE), aims to reconstruct high-fidelity signals from low-resolution (LR) or band-limited (BL) observations, an…
Token Reduction Should Go Beyond Efficiency in Generative Models -- From Vision, Language to Multimodality
Zhenglun Kong, Yize Li, Fanhu Zeng +7
In Transformer architectures, tokens\textemdash discrete units derived from raw data\textemdash are formed by segmenting inputs into fixed-length chunks. Each token is then mapped…
Pruning then Reweighting: Towards Data-Efficient Training of Diffusion Models
Yize Li, Yihua Zhang, Sijia Liu +1
Despite the remarkable generation capabilities of Diffusion Models (DMs), conducting training and inference remains computationally expensive. Previous works have been devoted to a…
Less is More: Data Pruning for Faster Adversarial Training
Yize Li, Pu Zhao, Xue Lin +2
Deep neural networks (DNNs) are sensitive to adversarial examples, resulting in fragile and unreliable performance in the real world. Although adversarial training (AT) is currentl…
Efficient Multi-Prize Lottery Tickets: Enhanced Accuracy, Training, and Inference Speed
Hao Cheng, Pu Zhao, Yize Li +4
Recently, Diffenderfer and Kailkhura proposed a new paradigm for learning compact yet highly accurate binary neural networks simply by pruning and quantizing randomly weighted full…
Reverse Engineering of Imperceptible Adversarial Image Perturbations
Yifan Gong, Yuguang Yao, Yize Li +4
It has been well recognized that neural network based image classifiers are easily fooled by images with tiny perturbations crafted by an adversary. There has been a vast volume of…