Search Results for author: Tejan Karmali

Found 6 papers, 2 papers with code

Improving GANs for Long-Tailed Data through Group Spectral Regularization

1 code implementation21 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.

Conditional Image Generation

Hierarchical Semantic Regularization of Latent Spaces in StyleGANs

no code implementations7 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.

Everything is There in Latent Space: Attribute Editing and Attribute Style Manipulation by StyleGAN Latent Space Exploration

no code implementations20 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.

Image Generation

LEAD: Self-Supervised Landmark Estimation by Aligning Distributions of Feature Similarity

no code implementations6 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.

Self-Supervised Learning

Deep Implicit Surface Point Prediction Networks

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.

Fashionable Modelling with Flux

2 code implementations1 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.

BIG-bench Machine Learning

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