40 citations · 51 across the 8 of their papers we have counts for
17 papers
Reducing Annotation Effort by Identifying and Labeling Contextually Diverse Classes for Semantic Segmentation Under Domain Shift
Sharat Agarwal, Saket Anand, Chetan Arora
In Active Domain Adaptation (ADA), one uses Active Learning (AL) to select a subset of images from the target domain, which are then annotated and used for supervised domain adapta…
Does Data Repair Lead to Fair Models? Curating Contextually Fair Data To Reduce Model Bias
Sharat Agarwal, Sumanyu Muku, Saket Anand +1
Contextual information is a valuable cue for Deep Neural Networks (DNNs) to learn better representations and improve accuracy. However, co-occurrence bias in the training dataset m…
Modeling Functional Similarity in Source Code with Graph-Based Siamese Networks
Nikita Mehrotra, Navdha Agarwal, Piyush Gupta +3
Code clones are duplicate code fragments that share (nearly) similar syntax or semantics. Code clone detection plays an important role in software maintenance, code refactoring, an…
Contextual Diversity for Active Learning
Sharat Agarwal, Himanshu Arora, Saket Anand +1
Requirement of large annotated datasets restrict the use of deep convolutional neural networks (CNNs) for many practical applications. The problem can be mitigated by using active…
DGSAC: Density Guided Sampling and Consensus
Lokender Tiwari, Saket Anand
Robust multiple model fitting plays a crucial role in many computer vision applications. Unlike single model fitting problems, the multi-model fitting has additional challenges. Th…
GraCIAS: Grassmannian of Corrupted Images for Adversarial Security
Ankita Shukla, Pavan Turaga, Saket Anand
Input transformation based defense strategies fall short in defending against strong adversarial attacks. Some successful defenses adopt approaches that either increase the randomn…