13 citations · 16 across the 5 of their papers we have counts for
5 papers
Do not trust what you trust: Miscalibration in Semi-supervised Learning
Shambhavi Mishra, Balamurali Murugesan, Ismail Ben Ayed +2
State-of-the-art semi-supervised learning (SSL) approaches rely on highly confident predictions to serve as pseudo-labels that guide the training on unlabeled samples. An inherent…
Class and Region-Adaptive Constraints for Network Calibration
Balamurali Murugesan, Julio Silva-Rodriguez, Ismail Ben Ayed +1
In this work, we present a novel approach to calibrate segmentation networks that considers the inherent challenges posed by different categories and object regions. In particular,…
Neighbor-Aware Calibration of Segmentation Networks with Penalty-Based Constraints
Balamurali Murugesan, Sukesh Adiga Vasudeva, Bingyuan Liu +3
Ensuring reliable confidence scores from deep neural networks is of paramount significance in critical decision-making systems, particularly in real-world domains such as healthcar…
Deep learning based non-contact physiological monitoring in Neonatal Intensive Care Unit
Nicky Nirlipta Sahoo, Balamurali Murugesan, Ayantika Das +5
Preterm babies in the Neonatal Intensive Care Unit (NICU) have to undergo continuous monitoring of their cardiac health. Conventional monitoring approaches are contact-based, makin…
A deep cascade of ensemble of dual domain networks with gradient-based T1 assistance and perceptual refinement for fast MRI reconstruction
Balamurali Murugesan, Sriprabha Ramanarayanan, Sricharan Vijayarangan +3
Deep learning networks have shown promising results in fast magnetic resonance imaging (MRI) reconstruction. In our work, we develop deep networks to further improve the quantitati…