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Shayok Chakraborty
Researcher at Florida State University
Publications - 68
Citations - 2621
Shayok Chakraborty is an academic researcher from Florida State University. The author has contributed to research in topics: Active learning & Deep learning. The author has an hindex of 17, co-authored 60 publications receiving 1565 citations. Previous affiliations of Shayok Chakraborty include Indian Institute of Technology, Hyderabad & Carnegie Mellon University.
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Proceedings ArticleDOI
Deep Hashing Network for Unsupervised Domain Adaptation
TL;DR: In this article, the authors proposed a novel deep learning framework that can exploit labeled source data and unlabeled target data to learn informative hash codes, to accurately classify unseen target data.
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Deep Hashing Network for Unsupervised Domain Adaptation
TL;DR: This is the first research effort to exploit the feature learning capabilities of deep neural networks to learn representative hash codes to address the domain adaptation problem and proposes a novel deep learning framework that can exploit labeled source data and unlabeled target data to learn informative hash codes, to accurately classify unseen target data.
Proceedings ArticleDOI
Multimodal emotion recognition using deep learning architectures
TL;DR: A database of multimodal recordings of actors enacting various expressions of emotions, which consists of audio and video sequences of actors displaying three different intensities of expressions of 23 different emotions along with facial feature tracking, skeletal tracking and the corresponding physiological data is presented.
Journal ArticleDOI
Deep-Learning Systems for Domain Adaptation in Computer Vision: Learning Transferable Feature Representations
TL;DR: The latest research in domain adaptation using deep neural networks and a brief survey of nondeep-learning techniques are outlined, which highlights some drawbacks with the current state of research in this area and offers directions for future research.
Journal ArticleDOI
Active Batch Selection via Convex Relaxations with Guaranteed Solution Bounds
TL;DR: This paper proposes two novel batch mode active learning (BMAL) algorithms: BatchRank and BatchRand, and is the first research effort to derive mathematical guarantees on the solution quality of the BMAL problem.