TA2.L6.1
SET-NAS: Sample-Efficient Training for Neural Architecture Search with Strong Predictor and Stratified Sampling
Yu-Ming Zhang, National Central University, Taiwan; Jun-Wei Hsieh, National Yang Ming Chiao Tung University, Taiwan; Yu-Hsiu Chang, National Central University, Taiwan; Xin Li, Ming-Ching Chang, University at Albany, China; Chun-Chieh Lee, Kuo-Chin Fan, National Central University, Taiwan
Session:
TA2.L6: Training and Supervision Strategies for Image & Video Data - IV Lecture
Track:
Visual Artificial Intelligence
Location:
Capital Suite - 18
Presentation Time:
Tue, 29 Oct, 10:30 - 10:48 Gulf Standard Time (UTC +4)
Session Chair:
Jenni Raitoharju, University of Jyväskylä
Session TA2.L6
TA2.L6.1: SET-NAS: Sample-Efficient Training for Neural Architecture Search with Strong Predictor and Stratified Sampling
Yu-Ming Zhang, National Central University, Taiwan; Jun-Wei Hsieh, National Yang Ming Chiao Tung University, Taiwan; Yu-Hsiu Chang, National Central University, Taiwan; Xin Li, Ming-Ching Chang, University at Albany, China; Chun-Chieh Lee, Kuo-Chin Fan, National Central University, Taiwan
TA2.L6.2: CONTEXTUALITY HELPS REPRESENTATION LEARNING FOR GENERALIZED CATEGORY DISCOVERY
Tingzhang Luo, Mingxuan Du, Jiatao Shi, Xinxiang Chen, China University of Geosciences, China; Bingchen Zhao, University of Edinburgh, China; Shaoguang Huang, China University of Geosciences, China
TA2.L6.3: UIMT: A framework for improving unimodal inference via multimodal training
Kateryna Chumachenko, Tampere University, Finland; Alexandros Iosifidis, Aarhus University, Finland; Moncef Gabbouj, Tampere University, Finland
TA2.L6.4: BOX-LEVEL CLASS-BALANCED SAMPLING FOR ACTIVE OBJECT DETECTION
Jingyi Liao, Xun Xu, Chuan Sheng Foo, Lile Cai, Institute for Infocomm Research (I2R), A*STAR, Singapore
TA2.L6.5: RSUD20K: A Dataset for Road Scene Understanding In Autonomous Driving
Hasib Zunair, Shakib Khan, Abdessamad Ben Hamza, Concordia University, Canada
Contacts