6 papers
AMRD: Adaptive Multi-Teacher Relational Distillation for Lightweight Speech Emotion Recognition
Yuqi Li, Yi-Cheng Lin, Xianglong Wang +5
On-device speech emotion recognition (SER) is critical for real-time applications, yet large self-supervised models that excel at SER are too costly for edge devices. Multi-teacher…
Leveraging large multimodal models for audio-video deepfake detection: a pilot study
Songjun Cao, Yuqi Li, Yunpeng Luo +2
Audio-visual deepfake detection (AVD) is increasingly important as modern generators can fabricate convincing speech and video. Most current multimodal detectors are small, task-sp…
SGLP: A Similarity Guided Fast Layer Partition Pruning for Compressing Large Deep Models
Yuqi Li, Yao Lu, Junhao Dong +7
Layer pruning has emerged as a potent approach to remove redundant layers in the pre-trained network on the purpose of reducing network size and improve computational efficiency. H…
The Structural Scalpel: Automated Contiguous Layer Pruning for Large Language Models
Yao Lu, Yuqi Li, Wenbin Xie +4
Although large language models (LLMs) have achieved revolutionary breakthroughs in many fields, their large model size and high computational cost pose significant challenges for p…
LoRALib: A Standardized Benchmark for Evaluating LoRA-MoE Methods
Shaoheng Wang, Yao Lu, Yuqi Li +5
As a parameter efficient fine-tuning (PEFT) method, low-rank adaptation (LoRA) can save significant costs in storage and computing, but its strong adaptability to a single task is…
SepPrune: Structured Pruning for Efficient Deep Speech Separation
Yuqi Li, Kai Li, Xin Yin +6
Although deep learning has substantially advanced speech separation in recent years, most existing studies continue to prioritize separation quality while overlooking computational…