Disentangled Lifespan Face Synthesis

Last update: Sep 20, 2022

Related tags

Deep Learning DLFS

Overview

Disentangled Lifespan Face Synthesis

Project Page | Paper

Demo on Colab

Preparation

Please follow this github to prepare the environments and dataset.

Training and Testing (link to the pretrained models in the colab)

training (please modify --dataroot, --name):

sh train_distan.sh

testing (please modify --dataroot, --name, --which_epoch, and --checkpoing_dir):

sh test_distan.sh

Reference

If you find this repo helpful, please consider citing:

@inproceedings{he2021dlfs,
  title={Disentangled Lifespan Face Synthesis},
  author={He, Sen and Liao, Wentong and Yang, Michael and Song, Yi-Zhe and Rosenhahn, Bodo and Xiang, Tao},
  booktitle={ICCV},
  year={2021}
}

Acknowledgements

This repository is based on LFS.

Owner

何森

Research fellow at CVSSP, University of Surrey, UK.

GitHub Repository

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Disentangled Lifespan Face Synthesis

Related tags

Overview

Disentangled Lifespan Face Synthesis

Project Page | Paper

Demo on Colab

Preparation

Training and Testing (link to the pretrained models in the colab)

Reference

Acknowledgements

Owner

何森

MINERVA: An out-of-the-box GUI tool for offline deep reinforcement learning

"Reinforcement Learning for Bandit Neural Machine Translation with Simulated Human Feedback"

Official PyTorch implementation of "Camera Distance-aware Top-down Approach for 3D Multi-person Pose Estimation from a Single RGB Image", ICCV 2019

An all-in-one application to visualize multiple different local path planning algorithms

A Pytorch loader for MVTecAD dataset.

Classification Modeling: Probability of Default

Official code for Next Check-ins Prediction via History and Friendship on Location-Based Social Networks (MDM 2018)

Semantically Contrastive Learning for Low-light Image Enhancement

Bayesian Inference Tools in Python

ICCV2021 - A New Journey from SDRTV to HDRTV.

PAMI stands for PAttern MIning. It constitutes several pattern mining algorithms to discover interesting patterns in transactional/temporal/spatiotemporal databases

[CIKM 2019] Code and dataset for "Fi-GNN: Modeling Feature Interactions via Graph Neural Networks for CTR Prediction"

Everything you want about DP-Based Federated Learning, including Papers and Code. (Mechanism: Laplace or Gaussian, Dataset: femnist, shakespeare, mnist, cifar-10 and fashion-mnist. )

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