33 citations · 212 across the 23 of their papers we have counts for
10 papers · 1 filter
Boosting Discriminative Visual Representation Learning with Scenario-Agnostic Mixup
Siyuan Li, Zicheng Liu, Zedong Wang +3
Mixup is a well-known data-dependent augmentation technique for DNNs, consisting of two sub-tasks: mixup generation and classification. However, the recent dominant online training…
GenURL: A General Framework for Unsupervised Representation Learning
Siyuan Li, Zicheng Liu, Zelin Zang +3
Unsupervised representation learning (URL), which learns compact embeddings of high-dimensional data without supervision, has made remarkable progress recently. However, the develo…
CSP-Net: Joint Compression and Classification Network for Epilepsy Seizure Prediction
Di Wu, Yi Shi, Ziyu Wang +2
Recent development in brain-machine interface technology has made seizure prediction possible. However, the communication of large volume of electrophysiological signals between se…
WenetSpeech: A 10000+ Hours Multi-domain Mandarin Corpus for Speech Recognition
Binbin Zhang, Hang Lv, Pengcheng Guo +9
In this paper, we present WenetSpeech, a multi-domain Mandarin corpus consisting of 10000+ hours high-quality labeled speech, 2400+ hours weakly labeled speech, and about 10000 hou…
Exploring Localization for Self-supervised Fine-grained Contrastive Learning
Di Wu, Siyuan Li, Zelin Zang +1
Self-supervised contrastive learning has demonstrated great potential in learning visual representations. Despite their success in various downstream tasks such as image classifica…
U2++: Unified Two-pass Bidirectional End-to-end Model for Speech Recognition
Di Wu, Binbin Zhang, Chao Yang +4
The unified streaming and non-streaming two-pass (U2) end-to-end model for speech recognition has shown great performance in terms of streaming capability, accuracy, real-time fact…
Unsupervised Deep Manifold Attributed Graph Embedding
Zelin Zang, Siyuan Li, Di Wu +3
Unsupervised attributed graph representation learning is challenging since both structural and feature information are required to be represented in the latent space. Existing meth…
Representation range needs for 16-bit neural network training
Valentina Popescu, Abhinav Venigalla, Di Wu +1
Deep learning has grown rapidly thanks to its state-of-the-art performance across a wide range of real-world applications. While neural networks have been trained using IEEE-754 bi…
AutoMix: Unveiling the Power of Mixup for Stronger Classifiers
Zicheng Liu, Siyuan Li, Di Wu +4
Data mixing augmentation have proved to be effective in improving the generalization ability of deep neural networks. While early methods mix samples by hand-crafted policies (e.g.…
WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit
Zhuoyuan Yao, Di Wu, Xiong Wang +7
In this paper, we propose an open source, production first, and production ready speech recognition toolkit called WeNet in which a new two-pass approach is implemented to unify st…