celeba - 技术专题

[CVPR2020] Adversarial Latent Autoencoders

A large-scale face dataset for face parsing, recognition, generation and editing.

Tensorflow implementation of "BEGAN: Boundary Equilibrium Generative Adversarial Networks"

Experiments for understanding disentanglement in VAE latent representations

Pytorch implementation of β-VAE

Pytorch implementation of conditional Generative Adversarial Networks (cGAN) and conditional Deep Convolutional Generative Adversarial Networks (cDCGAN) for MNIST dataset

Pytorch implementation of Generative Adversarial Networks (GAN) and Deep Convolutional Generative Adversarial Networks (DCGAN) for MNIST and CelebA datasets

Implementations of (theoretical) generative adversarial networks and comparison without cherry-picking

Simple Implementation of many GAN models with PyTorch.

Python package with source code from the course "Creative Applications of Deep Learning w/ TensorFlow"

PyTorch Implementation of InfoGAN

Pytorch implementation of FactorVAE proposed in Disentangling by Factorising(http://arxiv.org/abs/1802.05983)

To obtain high resolution face images from CelebA

Get started with CelebA-HQ dataset in under 5 mins !

MSG-GAN: Multi-Scale Gradients GAN (Architecture inspired from ProGAN but doesn't use layer-wise growing)

Code repository for "Interpretable and Accurate Fine-grained Recognition via Region Grouping", CVPR 2020 (Oral)

各种深度学习结构、模型和技巧的集合

Modified h5tool.py make user getting celeba-HQ easier

Tensorflow implementation of different GANs and their comparisions

Example of vanilla VAE for face image generation at resolution 128x128 using pytorch.