22 citations · 51 across the 23 of their papers we have counts for
6 papers · 1 filter
DIAGNOSE: Avoiding Out-of-distribution Data using Submodular Information Measures
Suraj Kothawade, Akshit Srivastava, Venkat Iyer +2
Avoiding out-of-distribution (OOD) data is critical for training supervised machine learning models in the medical imaging domain. Furthermore, obtaining labeled medical data is di…
CLINICAL: Targeted Active Learning for Imbalanced Medical Image Classification
Suraj Kothawade, Atharv Savarkar, Venkat Iyer +3
Training deep learning models on medical datasets that perform well for all classes is a challenging task. It is often the case that a suboptimal performance is obtained on some cl…
Object-Level Targeted Selection via Deep Template Matching
Suraj Kothawade, Donna Roy, Michele Fenzi +3
Retrieving images with objects that are semantically similar to objects of interest (OOI) in a query image has many practical use cases. A few examples include fixing failures like…
Active Data Discovery: Mining Unknown Data using Submodular Information Measures
Suraj Kothawade, Shivang Chopra, Saikat Ghosh +1
Active Learning is a very common yet powerful framework for iteratively and adaptively sampling subsets of the unlabeled sets with a human in the loop with the goal of achieving la…
BASIL: Balanced Active Semi-supervised Learning for Class Imbalanced Datasets
Suraj Kothawade, Pavan Kumar Reddy, Ganesh Ramakrishnan +1
Current semi-supervised learning (SSL) methods assume a balance between the number of data points available for each class in both the labeled and the unlabeled data sets. However,…
PLATINUM: Semi-Supervised Model Agnostic Meta-Learning using Submodular Mutual Information
Changbin Li, Suraj Kothawade, Feng Chen +1
Few-shot classification (FSC) requires training models using a few (typically one to five) data points per class. Meta learning has proven to be able to learn a parametrized model…