2 citations · 4 across the 9 of their papers we have counts for
9 papers
Plugin Speech Enhancement: A Universal Speech Enhancement Framework Inspired by Dynamic Neural Network
Yanan Chen, Zihao Cui, Yingying Gao +3
The expectation to deploy a universal neural network for speech enhancement, with the aim of improving noise robustness across diverse speech processing tasks, faces challenges due…
Cascaded Multi-task Adaptive Learning Based on Neural Architecture Search
Yingying Gao, Shilei Zhang, Zihao Cui +2
Cascading multiple pre-trained models is an effective way to compose an end-to-end system. However, fine-tuning the full cascaded model is parameter and memory inefficient and our…
GenDistiller: Distilling Pre-trained Language Models based on Generative Models
Yingying Gao, Shilei Zhang, Zihao Cui +3
Self-supervised pre-trained models such as HuBERT and WavLM leverage unlabeled speech data for representation learning and offer significantly improve for numerous downstream tasks…
Fine-grained Recognition with Learnable Semantic Data Augmentation
Yifan Pu, Yizeng Han, Yulin Wang +3
Fine-grained image recognition is a longstanding computer vision challenge that focuses on differentiating objects belonging to multiple subordinate categories within the same meta…
MPPN: Multi-Resolution Periodic Pattern Network For Long-Term Time Series Forecasting
Xing Wang, Zhendong Wang, Kexin Yang +4
Long-term time series forecasting plays an important role in various real-world scenarios. Recent deep learning methods for long-term series forecasting tend to capture the intrica…
ESCL: Equivariant Self-Contrastive Learning for Sentence Representations
Jie Liu, Yixuan Liu, Xue Han +2
Previous contrastive learning methods for sentence representations often focus on insensitive transformations to produce positive pairs, but neglect the role of sensitive transform…