22 citations · 28 across the 4 of their papers we have counts for
4 papers
NoisyTwins: Class-Consistent and Diverse Image Generation through StyleGANs
Harsh Rangwani, Lavish Bansal, Kartik Sharma +3
StyleGANs are at the forefront of controllable image generation as they produce a latent space that is semantically disentangled, making it suitable for image editing and manipulat…
Improving GANs for Long-Tailed Data through Group Spectral Regularization
Harsh Rangwani, Naman Jaswani, Tejan Karmali +2
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 sa…
Hierarchical Semantic Regularization of Latent Spaces in StyleGANs
Tejan Karmali, Rishubh Parihar, Susmit Agrawal +4
Progress in GANs has enabled the generation of high-resolution photorealistic images of astonishing quality. StyleGANs allow for compelling attribute modification on such images vi…
Everything is There in Latent Space: Attribute Editing and Attribute Style Manipulation by StyleGAN Latent Space Exploration
Rishubh Parihar, Ankit Dhiman, Tejan Karmali +1
Unconstrained Image generation with high realism is now possible using recent Generative Adversarial Networks (GANs). However, it is quite challenging to generate images with a giv…