22 citations · 60 across the 22 of their papers we have counts for
7 papers · 1 filter
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…
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…
Do Not Forget to Attend to Uncertainty while Mitigating Catastrophic Forgetting
Vinod K Kurmi, Badri N. Patro, Venkatesh K. Subramanian +1
One of the major limitations of deep learning models is that they face catastrophic forgetting in an incremental learning scenario. There have been several approaches proposed to t…
Curriculum based Dropout Discriminator for Domain Adaptation
Vinod Kumar Kurmi, Vipul Bajaj, Venkatesh K Subramanian +1
Domain adaptation is essential to enable wide usage of deep learning based networks trained using large labeled datasets. Adversarial learning based techniques have shown their uti…
Looking back at Labels: A Class based Domain Adaptation Technique
Vinod Kumar Kurmi, Vinay P. Namboodiri
In this paper, we solve the problem of adapting classifiers across domains. We consider the problem of domain adaptation for multi-class classification where we are provided a labe…