13 citations · 19 across the 7 of their papers we have counts for
16 papers
Gradient Based Activations for Accurate Bias-Free Learning
Vinod K Kurmi, Rishabh Sharma, Yash Vardhan Sharma +1
Bias mitigation in machine learning models is imperative, yet challenging. While several approaches have been proposed, one view towards mitigating bias is through adversarial lear…
Prb-GAN: A Probabilistic Framework for GAN Modelling
Blessen George, Vinod K. Kurmi, Vinay P. Namboodiri
Generative adversarial networks (GANs) are very popular to generate realistic images, but they often suffer from the training instability issues and the phenomenon of mode loss. In…
Exploring Dropout Discriminator for Domain Adaptation
Vinod K Kurmi, Venkatesh K Subramanian, Vinay P. Namboodiri
Adaptation of a classifier to new domains is one of the challenging problems in machine learning. This has been addressed using many deep and non-deep learning based methods. Among…
Sensor-invariant Fingerprint ROI Segmentation Using Recurrent Adversarial Learning
Indu Joshi, Ayush Utkarsh, Riya Kothari +4
A fingerprint region of interest (roi) segmentation algorithm is designed to separate the foreground fingerprint from the background noise. All the learning based state-of-the-art…
Data Uncertainty Guided Noise-aware Preprocessing Of Fingerprints
Indu Joshi, Ayush Utkarsh, Riya Kothari +4
The effectiveness of fingerprint-based authentication systems on good quality fingerprints is established long back. However, the performance of standard fingerprint matching syste…
Mitigating Uncertainty of Classifier for Unsupervised Domain Adaptation
Shanu Kumar, Vinod Kumar Kurmi, Praphul Singh +1
Understanding unsupervised domain adaptation has been an important task that has been well explored. However, the wide variety of methods have not analyzed the role of a classifier…