M
Maneesh Singh
Researcher at Insurance Services Office
Publications - 117
Citations - 4026
Maneesh Singh is an academic researcher from Insurance Services Office. The author has contributed to research in topics: Computer science & Engineering. The author has an hindex of 21, co-authored 79 publications receiving 2942 citations. Previous affiliations of Maneesh Singh include Carnegie Mellon University & University of Illinois at Urbana–Champaign.
Papers
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Book ChapterDOI
Diverse Image-to-Image Translation via Disentangled Representations
TL;DR: In this paper, a disentangled representation for image-to-image translation is proposed, which embeds images onto two spaces: a domain-invariant content space capturing shared information across domains and a domain specific attribute space.
Journal ArticleDOI
Diverse Image-to-Image Translation via Disentangled Representations
TL;DR: This work presents an approach based on disentangled representation for producing diverse outputs without paired training images, and proposes to embed images onto two spaces: a domain-invariant content space capturing shared information across domains and adomain-specific attribute space.
Proceedings ArticleDOI
Unsupervised Representation Learning by Sorting Sequences
TL;DR: In this article, the authors leverage the temporal coherence as a supervisory signal by formulating representation learning as a sequence sorting task and train a convolutional neural network to sort the shuffled sequences.
Posted Content
DRIT++: Diverse Image-to-Image Translation via Disentangled Representations
TL;DR: In this article, a disentangled representation is proposed for image-to-image translation without paired training images, which takes the encoded content features extracted from a given input and the attribute vectors sampled from the attribute space to produce diverse outputs at test time.
Posted Content
Unsupervised Representation Learning by Sorting Sequences
TL;DR: The experimental results show that the unsupervised representation learning approach using videos without semantic labels compares favorably against state-of-the-art methods on action recognition, image classification, and object detection tasks.