yogeshbalaji

Generate_To_Adapt

Implementation of "Generate To Adapt: Aligning Domains using Generative Adversarial Networks"

robustOT

Robust Optimal Transport code

Meta-Learning-Domain-Generalization

InvGAN

Official codebase of our paper "Invert and Defend: Model-based Approximate Inversion of Generative Adversarial Network For Secure Inference"

Adversarial-training

PGD adversarial training in PyTorch

Normalized-Wasserstein

Normalized Wasserstein for Mixture Distributions

EntropicGANs_meet_VAEs

Official implementation of "EntropicGANs meet VAEs: A Statistical Approach to Compute Sample Likelihoods in GANs"

CVPR17_Unrolling_the_shutter

Repository for the project "Unrolling the Shutter: CNN to Correct Motion Distortions"

Instance_Adaptive_Adversarial_Training

BigGAN-PyTorch

The author's officially unofficial PyTorch BigGAN implementation.

CIFAR10_training

Code for training CNN models on CIFAR dataset

dataflow

data feeder using tensorpack.dataflow

DynaMAZE

A maze game with dynamic maze connections

hackathon

eea hackathon

improved-diffusion

Release for Improved Denoising Diffusion Probabilistic Models

interactive_OT

Code for interactive visualization of optimal transport under mode imbalance

MGAN

Source code for the paper MGAN: Training Generative Adversarial Nets With Multiple Generators

models

Models and examples built with TensorFlow

nn

Progressive_training_GAN_pytorch

pytorch-maml

PyTorch implementation of MAML: https://arxiv.org/abs/1703.03400

taskonomy

Taskonomy: Disentangling Task Transfer Learning

torch7

http://torch.ch

Virtual_lab_project

This is the virtual lab project under Prof. Nitin Chandrachoodan

yogeshbalaji.github.io