18 citations · 37 across the 15 of their papers we have counts for
5 papers · 1 filter
HEX: Hierarchical Emergence Exploitation in Self-Supervised Algorithms
Kiran Kokilepersaud, Seulgi Kim, Mohit Prabhushankar +1
In this paper, we propose an algorithm that can be used on top of a wide variety of self-supervised (SSL) approaches to take advantage of hierarchical structures that emerge during…
CRACKS: Crowdsourcing Resources for Analysis and Categorization of Key Subsurface faults
Mohit Prabhushankar, Kiran Kokilepersaud, Jorge Quesada +6
Crowdsourcing annotations has created a paradigm shift in the availability of labeled data for machine learning. Availability of large datasets has accelerated progress in common k…
Ophthalmic Biomarker Detection: Highlights from the IEEE Video and Image Processing Cup 2023 Student Competition
Ghassan AlRegib, Mohit Prabhushankar, Kiran Kokilepersaud +5
The VIP Cup offers a unique experience to undergraduates, allowing students to work together to solve challenging, real-world problems with video and image processing techniques. I…
Explaining Representation Learning with Perceptual Components
Yavuz Yarici, Kiran Kokilepersaud, Mohit Prabhushankar +1
Self-supervised models create representation spaces that lack clear semantic meaning. This interpretability problem of representations makes traditional explainability methods inef…
Taxes Are All You Need: Integration of Taxonomical Hierarchy Relationships into the Contrastive Loss
Kiran Kokilepersaud, Yavuz Yarici, Mohit Prabhushankar +1
In this work, we propose a novel supervised contrastive loss that enables the integration of taxonomic hierarchy information during the representation learning process. A supervise…