MLSP-13.5
CONTRASTIVE PREDICTIVE CODING FOR ANOMALY DETECTION OF FETAL HEALTH FROM THE CARDIOTOCOGRAM
Ivar R. de Vries, Iris A.M. Huijben, Ruud J.G. van Sloun, Rik Vullings, Eindhoven University of Technology, Netherlands; René D. Kok, Nemo Healthcare BV, Netherlands
Session:
Self-supervised Learning Methods I
Track:
Machine Learning for Signal Processing
Location:
Gather Area H
Presentation Time:
Mon, 9 May, 20:00 - 20:45 China Time (UTC +8)
Mon, 9 May, 12:00 - 12:45 UTC
Mon, 9 May, 12:00 - 12:45 UTC
Session Chair:
Wenwu Wang, University of Surrey
Session MLSP-13
MLSP-13.1: VISUAL REPRESENTATION LEARNING WITH SELF-SUPERVISED ATTENTION FOR LOW-LABEL HIGH-DATA REGIME
Prarthana Bhattacharyya, University of Waterloo, Canada; Chenge Li, Xiaonan Zhao, István Fehérvári, Jason Sun, Amazon Inc., Canada
MLSP-13.2: TRIBYOL: TRIPLET BYOL FOR SELF-SUPERVISED REPRESENTATION LEARNING
Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, Hokkaido University, Japan
MLSP-13.3: SAGA: SELF-AUGMENTATION WITH GUIDED ATTENTION FOR REPRESENTATION LEARNING
Chun-Hsiao Yeh, Academia Sinica / UC Berkeley, Taiwan; Cheng-Yao Hong, Yen-Chi Hsu, Tyng-Luh Liu, Academia Sinica, Taiwan
MLSP-13.4: An Anomaly Detection Method Based on Self-supervised Learning With Soft Label Assignment for Defect Visual Inspection
Chuanfei Hu, Yongxiong Wang, University of Shanghai for Science and Technology, China
MLSP-13.5: CONTRASTIVE PREDICTIVE CODING FOR ANOMALY DETECTION OF FETAL HEALTH FROM THE CARDIOTOCOGRAM
Ivar R. de Vries, Iris A.M. Huijben, Ruud J.G. van Sloun, Rik Vullings, Eindhoven University of Technology, Netherlands; René D. Kok, Nemo Healthcare BV, Netherlands
MLSP-13.6: Graph Fine-Grained Contrastive Representation Learning
Hui Tang, Xun Liang, Yuhui Guo, Xiangping Zheng, Bo Wu, Renmin University of China, China