Topic
- Computing and Processing
- Components, Circuits, Devices and Systems
- Communication, Networking and Broadcast Technologies
- Power, Energy and Industry Applications
- Signal Processing and Analysis
- Robotics and Control Systems
- General Topics for Engineers
- Fields, Waves and Electromagnetics
- Engineered Materials, Dielectrics and Plasmas
- Bioengineering
- Transportation
- Photonics and Electrooptics
- Engineering Profession
- Aerospace
- Geoscience
- Nuclear Engineering
- Career Development
- Emerging Technologies
- Telecommunications
- English for Technical Professionals
Jae Won Cho
Affiliation
KAIST
Topic
Baseline Architecture,Cross-entropy Loss,Data Augmentation,Decision Boundary,Domain Adaptation,Generative Adversarial Networks,Latent Space,Loss Function,Self-supervised Learning,Target Model,Training Data,Visual Question Answering,1D Convolutional Layers,2D Convolutional Layers,2D Plane,3D Space,AI Models,Acquisition Function,Active Learning,Active Learning Methods,Additional Annotations,Additional Modifications,Adjacent Frames,Adversarial Examples,Adversarial Training,Alternative Models,Attack Methods,Attack Success,Attack Success Rate,Black-box Attacks,Bounding Box,Catastrophic Forgetting,Challenge Phase,Class Rank,Classification Layer,Classifier Discrepancy,Conjecture,Consistency Loss,Continuous Recognition,Continuous Sign Language Recognition,Contrastive Loss,Convolutional Layers,Data Augmentation Methods,Dataset Bias,Debiasing,Deep Neural Network,Distillation Loss,Diverse Categories,Diverse Sample,Division Ratio,
Biography
Jae Won Cho (Student Member, IEEE) received the B.S. degree in electrical engineering from the Georgia Institute of Technology, Atlanta, GA, USA, in 2018. He is currently pursuing the Ph.D. degree in electrical engineering with the Korea Advanced Institute of Science and Technology (KAIST) under the supervision of Professor In So Kweon. He was awarded a bronze prize from Samsung Humantech paper awards. His current research interests include deep learning topics, such as active learning and high-level computer vision application, such as vision and language.