[Pytorch] Generative retrieval model using semantic IDs from "Recommender Systems with Generative Retrieval"

The PyTorch implementation of Generative Pre-trained Transformers (GPTs) using Kolmogorov-Arnold Networks (KANs) for language modeling

PyTorch Implementation of DSB for Score Based Generative Modeling. Experiments managed using Hydra.

Implementation of Chroma, generative models of protein using DDPM and GNNs, in Pytorch

Pytorch source code for arXiv paper Neural Network Renormalization Group, a generative model using variational renormalization group and normalizing flow.

PyTorch Code used in 'Introduction to Deep Generative Modeling'

An **unofficial** pytorch implementation of using generative models to do quantum state tomography with POVM measurements.

A collection of 19 generative AI projects in Python, showcasing applications in text generation, image synthesis, and chatbots using frameworks like Transformers and PyTorch. Includes datasets, code, and tutorials for building and deploying cutting-edge AI models.

Official PyTorch Implementation of "Generative Approach for Probabilistic Human Mesh Recovery using Diffusion Models", ICCV 2023 CV4Metaverse Workshop

In this notebook, we'll be building a generative adversarial network (GAN) trained on the MNIST dataset. From this, we'll be able to generate new handwritten digits! GANs were first reported on in 2014 from Ian Goodfellow and others in Yoshua Bengio's lab. Since then, GANs have exploded in popularity. Here are a few examples to check out: Pix2Pix CycleGAN & Pix2Pix in PyTorch, Jun-Yan Zhu A list of generative models The idea behind GANs is that you have two networks, a generator 𝐺 and a discriminator 𝐷 , competing against each other. The generator makes "fake" data to pass to the discriminator. The discriminator also sees real training data and predicts if the data it's received is real or fake. The generator is trained to fool the discriminator, it wants to output data that looks as close as possible to real, training data. The discriminator is a classifier that is trained to figure out which data is real and which is fake. What ends up happening is that the generator learns to make data that is indistinguishable from real data to the discriminator. The general structure of a GAN is shown in the diagram above, using MNIST images as data. The latent sample is a random vector that the generator uses to construct its fake images. This is often called a latent vector and that vector space is called latent space. As the generator trains, it figures out how to map latent vectors to recognizable images that can fool the discriminator. If you're interested in generating only new images, you can throw out the discriminator after training. In this notebook, I'll show you how to define and train these adversarial networks in PyTorch and generate new images!

论文 Hinton等的论文 Matrix capsules with EM routing - Hinton, G. E., Sabour, S. and Frosst, N. (2018) Dynamic Routing Between Capsules - Sabour, S., Frosst, N. and Hinton, G.E. (2017) Transforming Auto-encoders - Hinton, G. E., Krizhevsky, A. and Wang, S. D. (2011) A parallel computation that assigns canonical object-based frames of reference. - Hinton, G.E. (1981) Shape representation in parallel systems - Hinton, G.E. (1981) Optimizing Neural Networks that Generate Images - Tijmen Tieleman’s disseration 其他论文 Capsule Network Performance on Complex Data - Xi, E., Bing, S. and Jin, Y. (2017) Accurate reconstruction of image stimuli from human fMRI based on the decoding model with capsule network architecture - Qiao, K., Zhang, C., Wang, L., Yan, B., Chen, J., Zeng, L. and Tong, L., (2018) An Optimization View on Dynamic Routing Between Capsules - Wang, D., Liu, E. (2018) CapsuleGAN: Generative Adversarial Capsule Network Ayush Jaiswal, Wael AbdAlmageed, Premkumar Natarajan. (2018) Spectral Capsule Networks - Bahadori, M. T. (2018) 博客 Max Pechyonkin的胶囊网络入门系列： 胶囊网络背后的直觉 胶囊如何工作 囊间动态路由算法 胶囊网络架构 基于TensorFlow实现胶囊网络 Debarko De的胶囊网络教程，包括注释详尽的胶囊网络实现代码 基于CUDA为胶囊网络实现TensorFlow定制操作 Jos van de Wolfshaar的文章，定制胶囊网络运算的CUDA支持 ISI新研究：胶囊生成对抗网络 用胶囊网络替换CNN作为GAN的判别网络，在MNIST数据集上取得了比卷积GAN更好的表现 Uncovering the Intuition behind Capsule Networks and Inverse Graphic Tanay Kothari的长篇教程 A Visual Representation of Capsule Connections in Dynamic Routing Between Capsules Mike Ross的胶囊网络示意图 Capsule Networks Are Shaking up AI — Here’s How to Use Them Nick Bourdakos的介绍 Capsule Networks Explained Kendrick Tan的解释 Understanding Dynamic Routing between Capsules (Capsule Networks) Jonathan Hui的教程，包括注释详尽的基于Keras的胶囊网络实现代码 Matrix capsules with EM routing Adrian Colyer关于EM路由的文章 Capsule Networks: A Glossary Sebastian Kwiatkowski的胶囊网络术语表 Overview of awesome articles 点评三篇胶囊网络教程 视频 Geoffrey Hinton’s talk: What is wrong with convolutional neural nets? - Geoffrey Hinton在MIT. Brain & Cognitive Sciences的演讲《卷积神经网络的问题在哪里？》 Capsule Networks (CapsNets) – Tutorial - “这视频棒极了。我本希望我能把胶囊解释得这么清楚。”Geoffrey Hinton Capsule networks: overview - 胶囊网络概览，包括向量和矩阵胶囊。 Overview of awesome videos 对以上3个视频的点评。 Capsule Networks: An Improvement to Convolutional Networks Siraj Raval介绍胶囊网络的视频 动态路由实现 官方实现 Sarasra/models 《Dynamic Routing Between Capsules》论文所用的代码 TensorFlow alisure-ml/CapsNet bourdakos1/capsule-networks etendue/CapsNet_TF InnerPeace-Wu/CapsNet-tensorflow jaesik817/adv_attack_capsnet jostosh/capsnet JunYeopLee/capsule-networks laodar/tf_CapsNet leoniloris/CapsNet naturomics/CapsNet-Tensorflow rrqq/CapsNet-tensorflow-jupyter thibo73800/capsnet-traffic-sign-classifier tjiang31/CapsNet winwinJJiang/capsNet-Tensorflow PyTorch acburigo/CapsNet adambielski/CapsNet-pytorch AlexHex7/CapsNet_pytorch aliasvishnu/Capsule-Networks-Notebook-MNIST andreaazzini/capsnet.pytorch cedrickchee/capsule-net-pytorch dragen1860/CapsNet-Pytorch gram-ai/capsule-networks higgsfield/Capsule-Network-Tutorial laubonghaudoi/CapsNet_guide_PyTorch leftthomas/CapsNet nishnik/CapsNet-PyTorch tonysy/CapsuleNet-PyTorch Ujjwal-9/CapsNet Keras fengwang/minimal-capsule gusgad/capsule-GAN mitiku1/Emopy-CapsNet ruslangrimov/capsnet-with-capsulewise-convolution streamride/CapsNet-keras-imdb sunxirui310/CapsNet-Keras theblackcat102/dynamic-routing-capsule-cifar XifengGuo/CapsNet-Keras XifengGuo/CapsNet-Fashion-MNIST Chainer soskek/dynamic_routing_between_capsules Torch mrkulk/Unsupervised-Capsule-Network MXNet AaronLeong/CapsNet_Mxnet GarrickLin/Capsnet.Gluon Soonhwan-Kwon/capsnet.mxnet CNTK Southworkscom/CapsNet-CNTK Lasagne DeniskaMazur/CapsNet-Lasagne Matlab yechengxi/LightCapsNet R dfalbel/capsnet JavaScript alseambusher/capsnet.js Vulcan moothyknight/CapsNet-for-Graphics-Rendering-Optimization EM路由实现 TensorFlow gyang274/capsulesEM www0wwwjs1/Matrix-Capsules-EM-Tensorflow PyTorch shzygmyx/Matrix-Capsules-pytorch 其他资源 Capsule Networks discussion Facebook讨论组 CapsNet-Tensorflow CapsNet-Tensorflow的gitter.im讨论组 Will capsule networks replace neural networks? Quora问答“胶囊网络会取代神经网络吗？” Could GANs work with Hinton’s capsule theory? Quora问答“GAN可以应用Hinton的胶囊理论吗？” Dynamic Routing Between Capsules Kyuhwan Jung对论文《Dynamic routing between Capsules》的评论（slideshare）

Generative Pretrained Transformer 2 (GPT-2) for Language Modeling using the PyTorch-Transformers library.

Neurips 2025. Official PyTorch implementation for the paper 'Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings'.

implement GANs and VAE using pytorch

Unofficial PyTorch implementation of LAPGAN (Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks, NIPS 2015)

Generic PyTorch Pipeline for solving Inverse Problems using Score-based Generative Models

A Generative Adversarial Networks(GAN) project template to simplify building and training deep learning models using pytorch.

Welcome to the "Image Generation from Scratch" repository! This project is dedicated to building image generation models from scratch using PyTorch. In this repository, you'll find both GANs (Generative Adversarial Networks) and Diffusion models implemented from the ground up.

Generate dogs images using DCGAN in PyTorch. https://medium.com/@shwetagoyal41/gans-a-brief-introduction-to-generative-adversarial-networks-f06216c7200e

Using Generative Adversarial Network models like CycleGAN and CollaGAN, the brain MRI images from BraTS dataset are augmented using pytorch framework.

PyTorch implementation of the paper "A Solution to the Dilemma for FSS Inverse Design Using Generative Models".

Generic PyTorch Pipeline for solving Inverse Problems using Score-based Generative Models

This project implements a Generative Adversarial Network (GAN) to enhance the quality of medical images such as MRI, CT, and X-ray scans. The model improves image resolution, reduces noise, and preserves structural details using deep learning and adversarial training. Built with Python, TensorFlow/PyTorch, and OpenCV

This project implements a simple Generative Adversarial Network (GAN) for generating handwritten digits from the MNIST dataset using PyTorch. GANs are a class of machine learning models composed of two neural networks, the Generator and the Discriminator, which are trained simultaneously in a competitive manner.

Variational Autoencoder (VAE) project using PyTorch, showcasing generative modeling through Fashion MNIST data encoding, decoding, and latent space exploration. Explore tasks like model implementation, training, visualization, and image generation.

This repository hosts PyTorch implementations for generating synthetic images using GANs. Explore various image generation techniques in computer vision. Perfect for learning and experimenting with generative modeling and image synthesis.

PyTorch implementation of GPT/GPT-2 from the original papers "Improving Language Understanding by Generative Pre-Training" and "Language Models are Unsupervised Multitask Learners". GPT is coded from scratch without use of PyTorch transformer classes. I present the results of training the model on part of The Pile dataset (21.5 bln tokens).

This repository contains a Retrieval Augmented Generation (RAG) system that leverages Vision Language Models (VLMs) to query visually rich documents. Built using FastAPI and PyTorch, the system processes PDFs, images, and text, and utilizes Google’s Generative AI for context-aware responses.

This repository is a hands-on replication of the Deep Convolutional GAN (DCGAN) to gain a solid understanding of its architecture and training dynamics. Built using PyTorch, it aims to provide a clear and educational implementation of how DCGANs generate realistic images from noise. Ideal for anyone exploring generative models.

Implemenation of Generative Models using PyTorch