no code implementations • 27 Nov 2023 • Rishubh Parihar, Prasanna Balaji, Raghav Magazine, Sarthak Vora, Tejan Karmali, Varun Jampani, R. Venkatesh Babu
We capitalize on disentangled latent spaces of pretrained GANs and train a Denoising Diffusion Probabilistic Model (DDPM) to learn the latent distribution for diverse edits.
1 code implementation • CVPR 2023 • Harsh Rangwani, Lavish Bansal, Kartik Sharma, Tejan Karmali, Varun Jampani, R. Venkatesh Babu
We find that one reason for degradation is the collapse of latents for each class in the $\mathcal{W}$ latent space.
Ranked #1 on Conditional Image Generation on ImageNet-LT
1 code implementation • 21 Aug 2022 • Harsh Rangwani, Naman Jaswani, Tejan Karmali, Varun Jampani, R. Venkatesh Babu
Deep long-tailed learning aims to train useful deep networks on practical, real-world imbalanced distributions, wherein most labels of the tail classes are associated with a few samples.
Ranked #1 on Image Generation on LSUN
no code implementations • 7 Aug 2022 • Tejan Karmali, Rishubh Parihar, Susmit Agrawal, Harsh Rangwani, Varun Jampani, Maneesh Singh, R. Venkatesh Babu
The quality of the generated images is predicated on two assumptions; (a) The richness of the hierarchical representations learnt by the generator, and, (b) The linearity and smoothness of the style spaces.
no code implementations • 20 Jul 2022 • Rishubh Parihar, Ankit Dhiman, Tejan Karmali, R. Venkatesh Babu
We propose a novel sampling method to sample latent from the manifold, enabling us to generate a diverse set of attribute styles beyond the styles present in the training set.
no code implementations • 6 Apr 2022 • Tejan Karmali, Abhinav Atrishi, Sai Sree Harsha, Susmit Agrawal, Varun Jampani, R. Venkatesh Babu
Existing works in self-supervised landmark detection are based on learning dense (pixel-level) feature representations from an image, which are further used to learn landmarks in a semi-supervised manner.
no code implementations • ICCV 2021 • Rahul Venkatesh, Tejan Karmali, Sarthak Sharma, Aurobrata Ghosh, R. Venkatesh Babu, László A. Jeni, Maneesh Singh
Unsigned distance function (UDF) based approaches have been proposed recently as a promising alternative to represent both open and closed shapes.
2 code implementations • 1 Nov 2018 • Michael Innes, Elliot Saba, Keno Fischer, Dhairya Gandhi, Marco Concetto Rudilosso, Neethu Mariya Joy, Tejan Karmali, Avik Pal, Viral Shah
Machine learning as a discipline has seen an incredible surge of interest in recent years due in large part to a perfect storm of new theory, superior tooling, renewed interest in its capabilities.