Collection of generative models in Tensorflow

Overview

tensorflow-generative-model-collections

Tensorflow implementation of various GANs and VAEs.

Related Repositories

Pytorch version

Pytorch version of this repository is availabel at https://github.com/znxlwm/pytorch-generative-model-collections

"Are GANs Created Equal? A Large-Scale Study" Paper

https://github.com/google/compare_gan is the code that was used in the paper.
It provides IS/FID and rich experimental results for all gan-variants.

Generative Adversarial Networks (GANs)

Lists

Name Paper Link Value Function
GAN Arxiv
LSGAN Arxiv
WGAN Arxiv
WGAN_GP Arxiv
DRAGAN Arxiv
CGAN Arxiv
infoGAN Arxiv
ACGAN Arxiv
EBGAN Arxiv
BEGAN Arxiv

Variants of GAN structure

Results for mnist

Network architecture of generator and discriminator is the exaclty sames as in infoGAN paper.
For fair comparison of core ideas in all gan variants, all implementations for network architecture are kept same except EBGAN and BEGAN. Small modification is made for EBGAN/BEGAN, since those adopt auto-encoder strucutre for discriminator. But I tried to keep the capacity of discirminator.

The following results can be reproduced with command:

python main.py --dataset mnist --gan_type 
   
     --epoch 25 --batch_size 64

   

Random generation

All results are randomly sampled.

Name Epoch 2 Epoch 10 Epoch 25
GAN
LSGAN
WGAN
WGAN_GP
DRAGAN
EBGAN
BEGAN

Conditional generation

Each row has the same noise vector and each column has the same label condition.

Name Epoch 1 Epoch 10 Epoch 25
CGAN
ACGAN
infoGAN

InfoGAN : Manipulating two continous codes

Results for fashion-mnist

Comments on network architecture in mnist are also applied to here.
Fashion-mnist is a recently proposed dataset consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes. (T-shirt/top, Trouser, Pullover, Dress, Coat, Sandal, Shirt, Sneaker, Bag, Ankle boot)

The following results can be reproduced with command:

python main.py --dataset fashion-mnist --gan_type 
   
     --epoch 40 --batch_size 64

   

Random generation

All results are randomly sampled.

Name Epoch 1 Epoch 20 Epoch 40
GAN
LSGAN
WGAN
WGAN_GP
DRAGAN
EBGAN
BEGAN

Conditional generation

Each row has the same noise vector and each column has the same label condition.

Name Epoch 1 Epoch 20 Epoch 40
CGAN
ACGAN
infoGAN

Without hyper-parameter tuning from mnist-version, ACGAN/infoGAN does not work well as compared with CGAN.
ACGAN tends to fall into mode-collapse.
infoGAN tends to ignore noise-vector. It results in that various style within the same class can not be represented.

InfoGAN : Manipulating two continous codes

Some results for celebA

(to be added)

Variational Auto-Encoders (VAEs)

Lists

Name Paper Link Loss Function
VAE Arxiv
CVAE Arxiv
DVAE Arxiv (to be added)
AAE Arxiv (to be added)

Variants of VAE structure

Results for mnist

Network architecture of decoder(generator) and encoder(discriminator) is the exaclty sames as in infoGAN paper. The number of output nodes in encoder is different. (2x z_dim for VAE, 1 for GAN)

The following results can be reproduced with command:

python main.py --dataset mnist --gan_type 
   
     --epoch 25 --batch_size 64

   

Random generation

All results are randomly sampled.

Name Epoch 1 Epoch 10 Epoch 25
VAE
GAN

Results of GAN is also given to compare images generated from VAE and GAN. The main difference (VAE generates smooth and blurry images, otherwise GAN generates sharp and artifact images) is cleary observed from the results.

Conditional generation

Each row has the same noise vector and each column has the same label condition.

Name Epoch 1 Epoch 10 Epoch 25
CVAE
CGAN

Results of CGAN is also given to compare images generated from CVAE and CGAN.

Learned manifold

The following results can be reproduced with command:

python main.py --dataset mnist --gan_type VAE --epoch 25 --batch_size 64 --dim_z 2

Please notice that dimension of noise-vector z is 2.

Name Epoch 1 Epoch 10 Epoch 25
VAE

Results for fashion-mnist

Comments on network architecture in mnist are also applied to here.

The following results can be reproduced with command:

python main.py --dataset fashion-mnist --gan_type 
   
     --epoch 40 --batch_size 64

   

Random generation

All results are randomly sampled.

Name Epoch 1 Epoch 20 Epoch 40
VAE
GAN

Results of GAN is also given to compare images generated from VAE and GAN.

Conditional generation

Each row has the same noise vector and each column has the same label condition.

Name Epoch 1 Epoch 20 Epoch 40
CVAE
CGAN

Results of CGAN is also given to compare images generated from CVAE and CGAN.

Learned manifold

The following results can be reproduced with command:

python main.py --dataset fashion-mnist --gan_type VAE --epoch 25 --batch_size 64 --dim_z 2

Please notice that dimension of noise-vector z is 2.

Name Epoch 1 Epoch 10 Epoch 25
VAE

Results for celebA

(to be added)

Folder structure

The following shows basic folder structure.

├── main.py # gateway
├── data
│   ├── mnist # mnist data (not included in this repo)
│   |   ├── t10k-images-idx3-ubyte.gz
│   |   ├── t10k-labels-idx1-ubyte.gz
│   |   ├── train-images-idx3-ubyte.gz
│   |   └── train-labels-idx1-ubyte.gz
│   └── fashion-mnist # fashion-mnist data (not included in this repo)
│       ├── t10k-images-idx3-ubyte.gz
│       ├── t10k-labels-idx1-ubyte.gz
│       ├── train-images-idx3-ubyte.gz
│       └── train-labels-idx1-ubyte.gz
├── GAN.py # vanilla GAN
├── ops.py # some operations on layer
├── utils.py # utils
├── logs # log files for tensorboard to be saved here
└── checkpoint # model files to be saved here

Acknowledgements

This implementation has been based on this repository and tested with Tensorflow over ver1.0 on Windows 10 and Ubuntu 14.04.

Official Pytorch Implementation for Splicing ViT Features for Semantic Appearance Transfer presenting Splice

Splicing ViT Features for Semantic Appearance Transfer [Project Page] Splice is a method for semantic appearance transfer, as described in Splicing Vi

Omer Bar Tal 253 Jan 06, 2023
General purpose Slater-Koster tight-binding code for electronic structure calculations

tight-binder Introduction General purpose tight-binding code for electronic structure calculations based on the Slater-Koster approximation. The code

9 Dec 15, 2022
Implementation for Stankevičiūtė et al. "Conformal time-series forecasting", NeurIPS 2021.

Conformal time-series forecasting Implementation for Stankevičiūtė et al. "Conformal time-series forecasting", NeurIPS 2021. If you use our code in yo

Kamilė Stankevičiūtė 36 Nov 21, 2022
Official implementation of NeuralFusion: Online Depth Map Fusion in Latent Space

NeuralFusion This is the official implementation of NeuralFusion: Online Depth Map Fusion in Latent Space. We provide code to train the proposed pipel

53 Jan 01, 2023
UmlsBERT: Clinical Domain Knowledge Augmentation of Contextual Embeddings Using the Unified Medical Language System Metathesaurus

UmlsBERT: Clinical Domain Knowledge Augmentation of Contextual Embeddings Using the Unified Medical Language System Metathesaurus General info This is

71 Oct 25, 2022
[NeurIPS 2021] Well-tuned Simple Nets Excel on Tabular Datasets

[NeurIPS 2021] Well-tuned Simple Nets Excel on Tabular Datasets Introduction This repo contains the source code accompanying the paper: Well-tuned Sim

52 Jan 04, 2023
This is the code for HOI Transformer

HOI Transformer Code for CVPR 2021 accepted paper End-to-End Human Object Interaction Detection with HOI Transformer. Reproduction We recomend you to

BigBangEpoch 124 Dec 29, 2022
Learning trajectory representations using self-supervision and programmatic supervision.

Trajectory Embedding for Behavior Analysis (TREBA) Implementation from the paper: Jennifer J. Sun, Ann Kennedy, Eric Zhan, David J. Anderson, Yisong Y

58 Jan 06, 2023
Invasive Plant Species Identification

Invasive_Plant_Species_Identification Used LiDAR Odometry and Mapping (LOAM) to create a 3D point cloud map which can be used to identify invasive pla

2 May 12, 2022
Research code for CVPR 2021 paper "End-to-End Human Pose and Mesh Reconstruction with Transformers"

MeshTransformer ✨ This is our research code of End-to-End Human Pose and Mesh Reconstruction with Transformers. MEsh TRansfOrmer is a simple yet effec

Microsoft 473 Dec 31, 2022
code for paper "Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning" by Zhongzheng Ren*, Raymond A. Yeh*, Alexander G. Schwing.

Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning Overview This code is for paper: Not All Unlabeled Data are Equa

Jason Ren 22 Nov 23, 2022
A Pythonic library for Nvidia Codec.

A Pythonic library for Nvidia Codec. The project is still in active development; expect breaking changes. Why another Python library for Nvidia Codec?

Zesen Qian 12 Dec 27, 2022
Nvdiffrast - Modular Primitives for High-Performance Differentiable Rendering

Nvdiffrast – Modular Primitives for High-Performance Differentiable Rendering Modular Primitives for High-Performance Differentiable Rendering Samuli

NVIDIA Research Projects 675 Jan 06, 2023
Deep Ensemble Learning with Jet-Like architecture

Ransomware analysis using DEL with jet-like architecture comprising two CNN wings, a sparse AE tail, a non-linear PCA to produce a diverse feature space, and an MLP nose

Ahsen Nazir 2 Feb 06, 2022
Tensorflow Implementation for "Pre-trained Deep Convolution Neural Network Model With Attention for Speech Emotion Recognition"

Tensorflow Implementation for "Pre-trained Deep Convolution Neural Network Model With Attention for Speech Emotion Recognition" Pre-trained Deep Convo

Ankush Malaker 5 Nov 11, 2022
Official repository for the NeurIPS 2021 paper Get Fooled for the Right Reason: Improving Adversarial Robustness through a Teacher-guided curriculum Learning Approach

Get Fooled for the Right Reason Official repository for the NeurIPS 2021 paper Get Fooled for the Right Reason: Improving Adversarial Robustness throu

Sowrya Gali 1 Apr 25, 2022
A general framework for inferring CNNs efficiently. Reduce the inference latency of MobileNet-V3 by 1.3x on an iPhone XS Max without sacrificing accuracy.

GFNet-Pytorch (NeurIPS 2020) This repo contains the official code and pre-trained models for the glance and focus network (GFNet). Glance and Focus: a

Rainforest Wang 169 Oct 28, 2022
Web service for facial landmark detection, head pose estimation, facial action unit recognition, and eye-gaze estimation based on OpenFace 2.0

OpenGaze: Web Service for OpenFace Facial Behaviour Analysis Toolkit Overview OpenFace is a fantastic tool intended for computer vision and machine le

Sayom Shakib 4 Nov 03, 2022
This repository contains the source code and data for reproducing results of Deep Continuous Clustering paper

Deep Continuous Clustering Introduction This is a Pytorch implementation of the DCC algorithms presented in the following paper (paper): Sohil Atul Sh

Sohil Shah 197 Nov 29, 2022
Doge-Prediction - Coding Club prediction ig

Doge-Prediction Coding Club prediction ig Basically: Create an application that

1 Jan 10, 2022