1 code implementation • 16 Dec 2023 • Kumar Shubham, Pranav Sastry, Prathosh AP
In our work, we address these challenges by (i) implementing a noise-aware classifier using the pseudo labels generated by the label model (ii) utilizing the noise-aware classifier's prediction to train the label model and generate class-conditional images.
no code implementations • 20 Mar 2023 • Piyush Tiwary, Kumar Shubham, Vivek Kashyap, Prathosh A. P
Bayesian Pseudo-Coreset (BPC) and Dataset Condensation are two parallel streams of work that construct a synthetic set such that, a model trained independently on this synthetic set, yields the same performance as training on the original training set.
no code implementations • 31 Aug 2021 • Ravi Kiran Reddy, Kumar Shubham, Gopalakrishnan Venkatesh, Sriram Gandikota, Sarthak Khoche, Dinesh Babu Jayagopi, Gopalakrishnan Srinivasaraghavan
Results show that our approach can generate semantic modifications on any real world facial image while preserving the identity.
1 code implementation • CLEF 2021 • Marcos V. Conde, Kumar Shubham, Prateek Agnihotri, Nitin D. Movva, Szilard Bessenyei
It is easier to hear birds than see them, however, they still play an essential role in nature and they are excellent indicators of deteriorating environmental quality and pollution.
no code implementations • 2 Nov 2020 • Kumar Shubham, Gopalakrishnan Venkatesh, Reijul Sachdev, Akshi, Dinesh Babu Jayagopi, G. Srinivasaraghavan
In our work, we have formulated a Markov Decision Process (MDP) over the latent space of a pre-trained GAN model to learn a conditional policy for semantic manipulation along specific attributes under defined identity bounds.