Search Results for author: Bo Qin

Found 6 papers, 1 papers with code

JEP-KD: Joint-Embedding Predictive Architecture Based Knowledge Distillation for Visual Speech Recognition

no code implementations4 Mar 2024 Chang Sun, Hong Yang, Bo Qin

Visual Speech Recognition (VSR) tasks are generally recognized to have a lower theoretical performance ceiling than Automatic Speech Recognition (ASR), owing to the inherent limitations of conveying semantic information visually.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +3

Weighted Joint Maximum Mean Discrepancy Enabled Multi-Source-Multi-Target Unsupervised Domain Adaptation Fault Diagnosis

no code implementations20 Oct 2023 Zixuan Wang, Haoran Tang, Haibo Wang, Bo Qin, Mark D. Butala, Weiming Shen, Hongwei Wang

Despite the remarkable results that can be achieved by data-driven intelligent fault diagnosis techniques, they presuppose the same distribution of training and test data as well as sufficient labeled data.

Unsupervised Domain Adaptation

Hard Sample Mining Enabled Supervised Contrastive Feature Learning for Wind Turbine Pitch System Fault Diagnosis

no code implementations26 Jun 2023 Zixuan Wang, Bo Qin, Mengxuan Li, Chenlu Zhan, Mark D. Butala, Peng Peng, Hongwei Wang

The proposed method employs cosine similarity to identify hard samples and subsequently, leverages supervised contrastive learning to learn more discriminative representations by constructing hard sample pairs.

Contrastive Learning Representation Learning

The RoyalFlush System for the WMT 2022 Efficiency Task

no code implementations3 Dec 2022 Bo Qin, Aixin Jia, Qiang Wang, Jianning Lu, Shuqin Pan, Haibo Wang, Ming Chen

This paper describes the submission of the RoyalFlush neural machine translation system for the WMT 2022 translation efficiency task.

Knowledge Distillation Machine Translation +1

Slow-varying Dynamics Assisted Temporal Capsule Network for Machinery Remaining Useful Life Estimation

no code implementations30 Mar 2022 Yan Qin, Chau Yuen, Yimin Shao, Bo Qin, XiaoLi Li

Similarly, the estimation accuracy of the milling machine has been improved by 23. 57% compared to LSTM and 19. 54% compared to CapsNet.

Time Series Time Series Analysis

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