7 citations · 21 across the 9 of their papers we have counts for
3 papers · 1 filter
Calibrating Deep Neural Networks using Explicit Regularisation and Dynamic Data Pruning
Ramya Hebbalaguppe, Rishabh Patra, Tirtharaj Dash +2
Deep neural networks (DNN) are prone to miscalibrated predictions, often exhibiting a mismatch between the predicted output and the associated confidence scores. Contemporary model…
A Novel Data Augmentation Technique for Out-of-Distribution Sample Detection using Compounded Corruptions
Ramya S. Hebbalaguppe, Soumya Suvra Goshal, Jatin Prakash +2
Modern deep neural network models are known to erroneously classify out-of-distribution (OOD) test data into one of the in-distribution (ID) training classes with high confidence.…
A Stitch in Time Saves Nine: A Train-Time Regularizing Loss for Improved Neural Network Calibration
Ramya Hebbalaguppe, Jatin Prakash, Neelabh Madan +1
Deep Neural Networks ( DNN s) are known to make overconfident mistakes, which makes their use problematic in safety-critical applications. State-of-the-art ( SOTA ) calibration tec…