All Projects → jacobgil → Keras Dcgan

jacobgil / Keras Dcgan

Keras implementation of Deep Convolutional Generative Adversarial Networks

Programming Languages

python
139335 projects - #7 most used programming language

Projects that are alternatives of or similar to Keras Dcgan

DCGAN-CelebA-PyTorch-CPP
DCGAN Implementation using PyTorch in both C++ and Python
Stars: ✭ 14 (-98.52%)
Mutual labels:  gan, dcgan
Pytorch Mnist Celeba Gan Dcgan
Pytorch implementation of Generative Adversarial Networks (GAN) and Deep Convolutional Generative Adversarial Networks (DCGAN) for MNIST and CelebA datasets
Stars: ✭ 363 (-61.51%)
Mutual labels:  gan, dcgan
Dcgan
The Simplest DCGAN Implementation
Stars: ✭ 286 (-69.67%)
Mutual labels:  gan, dcgan
DLSS
Deep Learning Super Sampling with Deep Convolutional Generative Adversarial Networks.
Stars: ✭ 88 (-90.67%)
Mutual labels:  gan, dcgan
Awesome Gans
Awesome Generative Adversarial Networks with tensorflow
Stars: ✭ 585 (-37.96%)
Mutual labels:  gan, dcgan
dcgan anime avatars
基于keras使用dcgan自动生成动漫头像
Stars: ✭ 37 (-96.08%)
Mutual labels:  gan, dcgan
Pycadl
Python package with source code from the course "Creative Applications of Deep Learning w/ TensorFlow"
Stars: ✭ 356 (-62.25%)
Mutual labels:  gan, dcgan
GANs-Keras
GANs Implementations in Keras
Stars: ✭ 24 (-97.45%)
Mutual labels:  gan, dcgan
Tf.gans Comparison
Implementations of (theoretical) generative adversarial networks and comparison without cherry-picking
Stars: ✭ 477 (-49.42%)
Mutual labels:  gan, dcgan
Mimicry
[CVPR 2020 Workshop] A PyTorch GAN library that reproduces research results for popular GANs.
Stars: ✭ 458 (-51.43%)
Mutual labels:  gan, dcgan
Unsupervised-Anomaly-Detection-with-Generative-Adversarial-Networks
Unsupervised Anomaly Detection with Generative Adversarial Networks on MIAS dataset
Stars: ✭ 95 (-89.93%)
Mutual labels:  gan, dcgan
Gans In Action
Companion repository to GANs in Action: Deep learning with Generative Adversarial Networks
Stars: ✭ 748 (-20.68%)
Mutual labels:  gan, dcgan
GAN-Project-2018
GAN in Tensorflow to be run via Linux command line
Stars: ✭ 21 (-97.77%)
Mutual labels:  gan, dcgan
Tensorflow DCGAN
Study Friendly Implementation of DCGAN in Tensorflow
Stars: ✭ 22 (-97.67%)
Mutual labels:  gan, dcgan
catgan pytorch
Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks
Stars: ✭ 50 (-94.7%)
Mutual labels:  gan, dcgan
Cat Generator
Generate cat images with neural networks
Stars: ✭ 354 (-62.46%)
Mutual labels:  gan, dcgan
Anogan Tf
Unofficial Tensorflow Implementation of AnoGAN (Anomaly GAN)
Stars: ✭ 218 (-76.88%)
Mutual labels:  gan, dcgan
Gan Tutorial
Simple Implementation of many GAN models with PyTorch.
Stars: ✭ 227 (-75.93%)
Mutual labels:  gan, dcgan
Igan
Interactive Image Generation via Generative Adversarial Networks
Stars: ✭ 3,845 (+307.74%)
Mutual labels:  gan, dcgan
Context Encoder
[CVPR 2016] Unsupervised Feature Learning by Image Inpainting using GANs
Stars: ✭ 731 (-22.48%)
Mutual labels:  gan, dcgan

KERAS-DCGAN ##Implementation of http://arxiv.org/abs/1511.06434 with the (awesome) keras library, for generating artificial images with deep learning.This trains two adversarial deep learning models on real images, in order to produce artificial images that look real.The generator model tries to produce images that look real and get a high score from the discriminator.The discriminator model tries to tell apart between real images and artificial images from the generator.---This assumes theano ordering.You can still use this with tensorflow, by setting "image_dim_ordering": "th" in ~/.keras/keras.json (although this will be slower).---## UsageTraining: python dcgan.py --mode train --batch_size <batch_size> python dcgan.py --mode train --path ~/images --batch_size 128Image generation:python dcgan.py --mode generate --batch_size <batch_size>``python dcgan.py --mode generate --batch_size <batch_size> --nice : top 5% images according to discriminatorpython dcgan.py --mode generate --batch_size 128---## Resultgenerated images : generated_image.pngnice_generated_image.pngtrain process :training_process.gif---

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].