To obtain high resolution face images from CelebA

PyTorch implementation of 'SDEdit' (Meng et al., 2021) from scratch and applying it to 'DDPM' on CelebA at 64 × 64

PyTorch implementation of 'DDIM' (Jiaming Song et al., 2020).

Face recognition in PyTorch.

A Novel Encoder-Decoder Approach for Text-to-Face Conversion using Generative Adversarial Networks and Attention Mechanism

Deep Learning Project on Diffusion Models for Image Generation

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

Apply Thatcher illusion on a set of face photos

PyTorch implementation of 'DDPM' (Ho et al., 2020) and training it on CelebA 64×64

Implement a GAN for Celeb_a dataset to generate celebrities faces with PyTorch

Implement a GAN for Celeb_a dataset to generate celebrities faces with expert mode of TensorFlow

Facial Attribute Recognition using the Transformer architecture, 91% on CelebA

Python-based software for potential assailants composite sketches generation with AI

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

PyTorch implementation of 'Improved DDPM' (Nichol and Dhariwal, 2021).

PyTorch implementation of 'DCGAN' (Radford et al., 2016) and training it on CelebA

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

[CVPR-2023] Re-thinking Model Inversion Attacks Against Deep Neural Networks

[AI6126] Advanced Computer Vision is an elective course of MSAI, SCSE, NTU, Singapore. The repository corresponds to the AI6126 of Semester 2, AY2021-2022, starting from 01/2022. The instructor of this course is Prof. Loy Chen Change and Prof. Liu Ziwei.

A Variational Autoencoder in PyTorch for the CelebA Dataset.