Variational auto-encoder trained on celebA . All rights reserved.

Code and notebooks related to the paper: "Reconstructing Faces from fMRI Patterns using Deep Generative Neural Networks" by VanRullen & Reddy, 2019

PyTorch implemented generative models for CelebA dataset: DCGAN, LSGAN, WGAN, WGANGP, InfoGAN, BEGAN, VAE, VAEGAN

Conditional VAE in Tensorflow 2 | Conditional Image Generation | CelebA dataset

A VAE for CelebA

A Variational Autoencoder in PyTorch for the CelebA Dataset.

Keras implementation of Variation Autoencoder for face generation. Analysis of the distribution of the latent space of the VAE. Vector arithemtic in the latent space. Morphing between the faces. The model was trained on CelebA dataset

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Minimal Discrete Variational Autoencoder (VQ-VAE) implementation in PyTorch, train, eval and test on Cifar10, CelebA, and ImageNet ILSVRC2012, get good result.

Recent work in the field of Explainable AI and Computer Vision on CNN based architecture has improved the interpretability of Deep Learning models and helped in visualizing the model pre- dictions. Methods like CAM, Grad-CAM and Guided Grad-CAM have proved the practicality of localized visual attention in the classification and categorization applications. However, not much research has been done on generative models. In our work, we implement Grad-CAM technique on VAE and CVAE models trained on CelebA-HQ dataset and calculate neural attention map. The aim of the project is to build generative models capable of generating controllable human faces and build semantic segmentation of human face, and then investigate methodologies to improve the explainability by applying Explainable AI tech- niques like Grad-CAM, and analysing the effect of altering the model architecture, loss functions, latent space size. Furthermore, we investigate the latent space information of models by modifying the latent node variables.

DCGAN, VAE using CIFAR-10 dataset and CelebA dataset

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Improved disentangling of VAE-GAN on CelebA

In this project I build and train VAEs model to produce new human faces, using CelebA data base.

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Vanilla Variational Autoencoder implementation in PyTorch.

本项目使用 PyTorch 对 CelebA 数据集进行训练，构建一个简单的 Variational Autoencoder (VAE)，并生成新的头像图像

Valinna VAE implemented in pytorch-lightning, trained through Celeba dataset

A comparative Generative AI framework for conditional face synthesis. Features a custom autoregressive Vision Mamba (SSM) architecture, benchmarked alongside Conditional DDPM and VAE models on the CelebA dataset.

Developed generative models using VAEs, GANs, Diffusion Models to generate images from the MNIST dataset. Implemented image-to-image translation with DCGANs and CycleGANs using the CelebA dataset. Conducted extensive experimentation with latent variable dimensions, leveraging libraries like TensorFlow, Keras for neural networks construction.

Implementing VAE in keras and training on CelebA dataset

Simple VAE implementation in tensorflow, jax and pytorch where both the encoder and decoder model use gaussian distributions.

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A Variational Autoencoder model is trained with CelebA dataset, which can reconstruct and generate new celebrity images. Latent space is also explored and changed in desired direction to change attributes of input images.

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VQ-VAE and Transformer for Image Generation Using CelebA Dataset

Build a debiased model for facial recognition and reveal more facial attributes from the CelebA dataset. Skills : Deep Learning, CNN, InceptionV3, VAE, DB-VAE

A PyTorch implementation of popular generative models for image synthesis, featuring VAE, DCGAN, and WGAN architectures trained on the CelebA dataset.