gsurma / Image_generator
Licence: mit
DCGAN image generator 🖼️.
Stars: ✭ 173
Programming Languages
Labels
Projects that are alternatives of or similar to Image generator
Generative adversarial networks 101
Keras implementations of Generative Adversarial Networks. GANs, DCGAN, CGAN, CCGAN, WGAN and LSGAN models with MNIST and CIFAR-10 datasets.
Stars: ✭ 138 (-20.23%)
Mutual labels: jupyter-notebook, gan, generative-adversarial-network, dcgan
Deep Learning With Python
Example projects I completed to understand Deep Learning techniques with Tensorflow. Please note that I do no longer maintain this repository.
Stars: ✭ 134 (-22.54%)
Mutual labels: jupyter-notebook, gan, generative-adversarial-network, dcgan
Gans In Action
Companion repository to GANs in Action: Deep learning with Generative Adversarial Networks
Stars: ✭ 748 (+332.37%)
Mutual labels: jupyter-notebook, gan, generative-adversarial-network, dcgan
Ganspace
Discovering Interpretable GAN Controls [NeurIPS 2020]
Stars: ✭ 1,224 (+607.51%)
Mutual labels: jupyter-notebook, gan, generative-adversarial-network
Relativistic Average Gan Keras
The implementation of Relativistic average GAN with Keras
Stars: ✭ 36 (-79.19%)
Mutual labels: jupyter-notebook, gan, generative-adversarial-network
Pix2pix
Image-to-image translation with conditional adversarial nets
Stars: ✭ 8,765 (+4966.47%)
Mutual labels: gan, generative-adversarial-network, dcgan
Awesome Gans
Awesome Generative Adversarial Networks with tensorflow
Stars: ✭ 585 (+238.15%)
Mutual labels: gan, generative-adversarial-network, dcgan
Spectralnormalizationkeras
Spectral Normalization for Keras Dense and Convolution Layers
Stars: ✭ 100 (-42.2%)
Mutual labels: jupyter-notebook, gan, generative-adversarial-network
Calogan
Generative Adversarial Networks for High Energy Physics extended to a multi-layer calorimeter simulation
Stars: ✭ 87 (-49.71%)
Mutual labels: jupyter-notebook, gan, generative-adversarial-network
Stylegan2 Projecting Images
Projecting images to latent space with StyleGAN2.
Stars: ✭ 102 (-41.04%)
Mutual labels: jupyter-notebook, gan, generative-adversarial-network
Capsule Gan
Code for my Master thesis on "Capsule Architecture as a Discriminator in Generative Adversarial Networks".
Stars: ✭ 120 (-30.64%)
Mutual labels: jupyter-notebook, gan, generative-adversarial-network
Face generator
DCGAN face generator 🧑.
Stars: ✭ 146 (-15.61%)
Mutual labels: jupyter-notebook, gan, dcgan
Context Encoder
[CVPR 2016] Unsupervised Feature Learning by Image Inpainting using GANs
Stars: ✭ 731 (+322.54%)
Mutual labels: gan, generative-adversarial-network, dcgan
Dcgan Tensorflow
A Tensorflow implementation of Deep Convolutional Generative Adversarial Networks trained on Fashion-MNIST, CIFAR-10, etc.
Stars: ✭ 70 (-59.54%)
Mutual labels: gan, generative-adversarial-network, dcgan
Fewshot Face Translation Gan
Generative adversarial networks integrating modules from FUNIT and SPADE for face-swapping.
Stars: ✭ 705 (+307.51%)
Mutual labels: jupyter-notebook, gan, generative-adversarial-network
Sprint gan
Privacy-preserving generative deep neural networks support clinical data sharing
Stars: ✭ 92 (-46.82%)
Mutual labels: jupyter-notebook, gan, generative-adversarial-network
Generative Adversarial Networks
Introduction to generative adversarial networks, with code to accompany the O'Reilly tutorial on GANs
Stars: ✭ 505 (+191.91%)
Mutual labels: jupyter-notebook, gan, generative-adversarial-network
Hidt
Official repository for the paper "High-Resolution Daytime Translation Without Domain Labels" (CVPR2020, Oral)
Stars: ✭ 513 (+196.53%)
Mutual labels: jupyter-notebook, gan, generative-adversarial-network
Psgan
Periodic Spatial Generative Adversarial Networks
Stars: ✭ 108 (-37.57%)
Mutual labels: jupyter-notebook, gan, dcgan
Semantic image inpainting
Semantic Image Inpainting
Stars: ✭ 140 (-19.08%)
Mutual labels: gan, generative-adversarial-network, dcgan
Image Generator
Python notebook containing TensorFlow DCGAN implementation. It was trained on a Simpsons Faces dataset.
Check out corresponding Kaggle kernel: Image Generator.
Check out corresponding Medium article:
Image Generator - Drawing Cartoons with Generative Adversarial Networks
DCGAN
Network architecture by Radford et al., 2015.
Training
Visualization of training with the following hyperparameteres.
IMAGE_SIZE = 128
NOISE_SIZE = 100
LR_D = 0.00004
LR_G = 0.0004
BATCH_SIZE = 64
EPOCHS = 300
BETA1 = 0.5
WEIGHT_INIT_STDDEV = 0.02
EPSILON = 0.00005
Results
Cherry-picked generated samples.
As expected, there were some funny-looking malformed faces as well.
Author
Greg (Grzegorz) Surma
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].