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PyTorch implementation of 2D Positional Encodings for Vision Transformers (ViT). Positional Encodings/Embeddings: Sinusoidal (Absolute), Learnable, Relative and Rotation (Rope).
Find all the duplicate images in a folder by pixel-wise comparison and deletes them. Repeats it for all sub-directories as well.
Official Pytorch code of Generative Alignment of Posterior Probabilities for Source-free Domain Adaptation
Official PyTorch Implementation of Glocal Alignment for Unsupervised Domain Adaptation
Official Pytorch Implementation of PatchRot: A Self-Supervised Technique for Training Vision Transformers
Official Pytorch code for PatchSwap: A Regularization Technique for Vision Transformers
Unofficial Pytorch implementation of CycleGAN for MNIST, USPS, SVHN, MNIST-M, and SyntheticDigits datasets.
Unofficial PyTorch implementation of Domain-Adversarial Training of Neural Networks
Pytorch implementation of DCGAN for generating 64x64 images.
Pytorch implementation of DCGAN for generating 32x32 images of SVHN, CIFAR10, MNIST, FashionMNIST, and USPS dataset.
From scratch, simple and easy-to-understand Pytorch implementation of variants of generative adversarial network (GAN). Implemented variants: Conditional GAN (cGAN), DCGAN, LSGAN. Datasets used MNIST, SVHN, FashionMNIST, CIFAR10, CelebA, LSUN-Bedroom, LSUN-Church.
PyTorch implementations of Generative Adversarial Networks (GAN) variants: Vanilla GAN, Nano GAN, WGAN, WGAN-GP, StarGAN
Pytorch implementation of LSGAN for generating 112x112images.
Pytorch implementation of LSGAN for generating MNIST images.
Unofficial PyTorch implementation of Maximum Domain Confusion loss for Unsupervised Domain Adaptation
Pytorch implementation of Neural Style Transfer
Simple and easy to understand PyTorch implementation of Vision Transformer (ViT) from scratch for small datasets like MNIST, FashionMNIST, SVHN and CIFAR10 with detailed steps.
Pytorch implementation of a small GAN network for MNIST, FashionMNIST, and USPS dataset.
Unofficial Pytorch implementation of StarGAN for generating Digit-5 datasets (MNIST, SVHN, SynDigits, MNIST-M, and USPS).
Pytorch implementation of Vanilla-GAN for MNIST, FashionMNIST, and USPS dataset.
Pytorch implementation of WGAN using DCGAN architecture for generating 64x64 images.
Pytorch implementation of Improved Training of Wasserstein GANs or WGAN-GP (Wasserstein GAN with Gradient Penalty) using DCGAN architecture for generating 64x64 images.
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.