PrasannaPulakurthi

Cricket-and-Coding-Challenge

By analysing the past data, the project aims to predict the powerplay score of each innings in a match.

ASL_Tutor

Automatic_License_Plate_Recognition

EHAR-GAN

Enhancing Human Action Recognition with GAN-based Data Augmentation

Image-Denoising

An Invertible Rescaling Network (IRN) is trained to perform single-image denoising.

MMD-AdversarialNAS

Official code for the paper "Enhancing GAN Performance Through Neural Architecture Search and Tensor Decomposition".

MMD-PMish-NAS

Official code for the paper "Enhancing GANs with MMD Neural Architecture Search, PMish Activation Function, and Adaptive Rank Decomposition".

mmdpmishnas

Official website code for the paper "Enhancing GANs with MMD Neural Architecture Search, PMish Activation Function, and Adaptive Rank Decomposition"

ProGAN_PyTorch

ProGAN (Progressive Growing of GANs) is a method for training GANs that progressively increases the resolution of generated images, starting from low resolution and growing layers incrementally for better stability and image quality.

PrasannaPulakurthi/MMD-AdversarialNAS

Official code for the paper "Enhancing GAN Performance Through Neural Architecture Search and Tensor Decomposition".

PrasannaPulakurthi/ASL_Tutor

PrasannaPulakurthi/EHAR-GAN

Enhancing Human Action Recognition with GAN-based Data Augmentation

PrasannaPulakurthi/Automatic_License_Plate_Recognition

PrasannaPulakurthi/Image-Denoising

An Invertible Rescaling Network (IRN) is trained to perform single-image denoising.

PrasannaPulakurthi/MMD-PMish-NAS

Official code for the paper "Enhancing GANs with MMD Neural Architecture Search, PMish Activation Function, and Adaptive Rank Decomposition".

PrasannaPulakurthi/mmdpmishnas

Official website code for the paper "Enhancing GANs with MMD Neural Architecture Search, PMish Activation Function, and Adaptive Rank Decomposition"

PrasannaPulakurthi/ProGAN_PyTorch

ProGAN (Progressive Growing of GANs) is a method for training GANs that progressively increases the resolution of generated images, starting from low resolution and growing layers incrementally for better stability and image quality.