18 citations · 37 across the 15 of their papers we have counts for
11 papers · 1 filter
Gradient based Severity Labeling for Biomarker Classification in OCT
Kiran Kokilepersaud, Mohit Prabhushankar, Ghassan AlRegib +2
In this paper, we propose a novel selection strategy for contrastive learning for medical images. On natural images, contrastive learning uses augmentations to select positive and…
Countering Multi-modal Representation Collapse through Rank-targeted Fusion
Seulgi Kim, Kiran Kokilepersaud, Mohit Prabhushankar +1
Multi-modal fusion methods often suffer from two types of representation collapse: feature collapse where individual dimensions lose their discriminative power (as measured by eige…
Subject Invariant Contrastive Learning for Human Activity Recognition
Yavuz Yarici, Kiran Kokilepersaud, Mohit Prabhushankar +1
The high cost of annotating data makes self-supervised approaches, such as contrastive learning methods, appealing for Human Activity Recognition (HAR). Effective contrastive learn…
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…
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…
FOCAL: A Cost-Aware Video Dataset for Active Learning
Kiran Kokilepersaud, Yash-Yee Logan, Ryan Benkert +6
In this paper, we introduce the FOCAL (Ford-OLIVES Collaboration on Active Learning) dataset which enables the study of the impact of annotation-cost within a video active learning…
Clinically Labeled Contrastive Learning for OCT Biomarker Classification
Kiran Kokilepersaud, Stephanie Trejo Corona, Mohit Prabhushankar +2
This paper presents a novel positive and negative set selection strategy for contrastive learning of medical images based on labels that can be extracted from clinical data. In the…
Exploiting the Distortion-Semantic Interaction in Fisheye Data
Kiran Kokilepersaud, Mohit Prabhushankar, Yavuz Yarici +2
In this work, we present a methodology to shape a fisheye-specific representation space that reflects the interaction between distortion and semantic context present in this data m…
Clinical Contrastive Learning for Biomarker Detection
Kiran Kokilepersaud, Mohit Prabhushankar, Ghassan AlRegib
This paper presents a novel positive and negative set selection strategy for contrastive learning of medical images based on labels that can be extracted from clinical data. In the…
Volumetric Supervised Contrastive Learning for Seismic Semantic Segmentation
Kiran Kokilepersaud, Mohit Prabhushankar, Ghassan AlRegib
In seismic interpretation, pixel-level labels of various rock structures can be time-consuming and expensive to obtain. As a result, there oftentimes exists a non-trivial quantity…