Non-Official Pytorch implementation of "Face Identity Disentanglement via Latent Space Mapping" https://arxiv.org/abs/2005.07728 Using StyleGAN2 instead of StyleGAN

Related tags

Deep Learning encoder pytorch stylegan2 stylegan-encoder stylegan-image-manipulation face-identity-disentanglement
Overview

Face Identity Disentanglement via Latent Space Mapping - Implement in pytorch with StyleGAN 2

Description

Pytorch implementation of the paper Face Identity Disentanglement via Latent Space Mapping for both training and evaluation, with StyleGAN 2.

Changes from original paper

  • instead of using a Discriminator loss for the mapper. We have used several other losses such as:
    • LPIPS Loss (The Unreasonable Effectiveness of Deep Features as a Perceptual Metric, Zhang el al, 2018)
    • MSE Loss
    • Different ID Loss
    • Different landmark detector
  • The reason for those changes resides in the fact that the training procedure with Discriminator is often hard and does not converge. We have found that replacing the Discriminator with LPIPS and MSE losses we can achieve the same result. Nevertheless, our code supports training with a discriminator which can be activated using the configuration.
  • The other changes are due to better Recognition models that have developed since the original paper was published

Setup

We used several pretrained models:

  • StyleGan2 Generator for image size 256 - 550000.pt
  • ID Encoder - model_ir_se50.pth
  • Landmarks Detection - mobilefacenet_model_best.pth.tar

Weight files attached at this Drive folder.

You can also find at the above link our environment.yml file to create a relevant conda environment.

Training

The dataset is comprised of StyleGAN 2 generated images and W latent codes. see Utils/data_creator.py.

Examples of our generated dataset attached at this Drive folder.

To train the model run train_script.py, you can change parameters in Configs/ folder.

Inference

Try Inference.ipynb notebook to disentangle identity from attributes by yourself

Checkpoints

Our pretrained checkpoint attached at this Drive folder.

Results

Results

You might also like...
PyTorch implementation of Asymmetric Siamese (https://arxiv.org/abs/2204.00613)
PyTorch implementation of Asymmetric Siamese (https://arxiv.org/abs/2204.00613)

Asym-Siam: On the Importance of Asymmetry for Siamese Representation Learning This is a PyTorch implementation of the Asym-Siam paper, CVPR 2022: @inp

Meta Research 89 Dec 18, 2022
Unofficial implementation of
Unofficial implementation of "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" (https://arxiv.org/abs/2103.14030)

Swin-Transformer-Tensorflow A direct translation of the official PyTorch implementation of "Swin Transformer: Hierarchical Vision Transformer using Sh

null 52 Dec 29, 2022
Official repository with code and data accompanying the NAACL 2021 paper "Hurdles to Progress in Long-form Question Answering" (https://arxiv.org/abs/2103.06332).

Hurdles to Progress in Long-form Question Answering This repository contains the official scripts and datasets accompanying our NAACL 2021 paper, "Hur

Kalpesh Krishna 41 Nov 8, 2022
Minimal implementation of PAWS (https://arxiv.org/abs/2104.13963) in TensorFlow.
Minimal implementation of PAWS (https://arxiv.org/abs/2104.13963) in TensorFlow.

PAWS-TF 🐾 Implementation of Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support Samples (PAWS)

Sayak Paul 43 Jan 8, 2023
Unofficial Tensorflow-Keras implementation of Fastformer based on paper [Fastformer: Additive Attention Can Be All You Need](https://arxiv.org/abs/2108.09084).
Unofficial Tensorflow-Keras implementation of Fastformer based on paper [Fastformer: Additive Attention Can Be All You Need](https://arxiv.org/abs/2108.09084).

Fastformer-Keras Unofficial Tensorflow-Keras implementation of Fastformer based on paper Fastformer: Additive Attention Can Be All You Need. Tensorflo

Yam Peleg 10 Jan 30, 2022
Tensorflow implementation of Semi-supervised Sequence Learning (https://arxiv.org/abs/1511.01432)
Tensorflow implementation of Semi-supervised Sequence Learning (https://arxiv.org/abs/1511.01432)

Transfer Learning for Text Classification with Tensorflow Tensorflow implementation of Semi-supervised Sequence Learning(https://arxiv.org/abs/1511.01

DONGJUN LEE 82 Oct 22, 2022
This repository contains the code used for Predicting Patient Outcomes with Graph Representation Learning (https://arxiv.org/abs/2101.03940).
This repository contains the code used for Predicting Patient Outcomes with Graph Representation Learning (https://arxiv.org/abs/2101.03940).

Predicting Patient Outcomes with Graph Representation Learning This repository contains the code used for Predicting Patient Outcomes with Graph Repre

Emma Rocheteau 76 Dec 22, 2022
https://arxiv.org/abs/2102.11005
https://arxiv.org/abs/2102.11005

LogME LogME: Practical Assessment of Pre-trained Models for Transfer Learning How to use Just feed the features f and labels y to the function, and yo

THUML: Machine Learning Group @ THSS 149 Dec 19, 2022
Supplementary code for the paper
Supplementary code for the paper "Meta-Solver for Neural Ordinary Differential Equations" https://arxiv.org/abs/2103.08561

Meta-Solver for Neural Ordinary Differential Equations Towards robust neural ODEs using parametrized solvers. Main idea Each Runge-Kutta (RK) solver w

Julia Gusak 25 Aug 12, 2021
Comments
  • Properly load inception model weights

    Properly load inception model weights

    The last layer in the network which resizes the data is removed in untrained versions of Inception.

    The checkpoint has this layer removed too but an unknown side effect causes it to throw 'Resize' object has no attribute 'max_size'.

    This issue is fixed by extracting the children like for an untrained network, except without removing the last layer.

    Fixes #4

    opened by Richienb 1
  • Attribute feature extraction failed

    Attribute feature extraction failed

    There is some error. File "/data0/cdx/ID-disentanglement-pytorch/Training/train_script.py", line 159, in attr_vec = torch.squeeze(attr_encoder(attr_images)) File "/home/amax/anaconda3/envs/cdx/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl return forward_call(*input, **kwargs) File "/data0/cdx/ID-disentanglement-pytorch/Models/Encoders/Inception.py", line 29, in forward return self.model(resized_data * 255) File "/home/amax/anaconda3/envs/cdx/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl return forward_call(*input, **kwargs) File "/home/amax/anaconda3/envs/cdx/lib/python3.8/site-packages/torch/nn/modules/container.py", line 141, in forward input = module(input) File "/home/amax/anaconda3/envs/cdx/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl return forward_call(*input, **kwargs) File "/home/amax/anaconda3/envs/cdx/lib/python3.8/site-packages/torchvision/models/inception.py", line 316, in forward outputs = self._forward(x) File "/home/amax/anaconda3/envs/cdx/lib/python3.8/site-packages/torchvision/models/inception.py", line 303, in _forward branch3x3 = self.branch3x3_1(x) File "/home/amax/anaconda3/envs/cdx/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl return forward_call(*input, **kwargs) File "/home/amax/anaconda3/envs/cdx/lib/python3.8/site-packages/torchvision/models/inception.py", line 405, in forward x = self.conv(x) File "/home/amax/anaconda3/envs/cdx/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl return forward_call(*input, **kwargs) File "/home/amax/anaconda3/envs/cdx/lib/python3.8/site-packages/torch/nn/modules/conv.py", line 447, in forward return self._conv_forward(input, self.weight, self.bias) File "/home/amax/anaconda3/envs/cdx/lib/python3.8/site-packages/torch/nn/modules/conv.py", line 443, in _conv_forward return F.conv2d(input, weight, bias, self.stride, RuntimeError: Expected 3D (unbatched) or 4D (batched) input to conv2d, but got input of size: [8, 1000]

    opened by winterplayer 0
  • train_script.py

    train_script.py

    in this train_script.py, when running,the console shows: " File "D:/PycharmProjects/ID-disentanglement-Pytorch-master/Training/train_script.py", line 116, in data = next(iter(test_loader)) "

    how to do it?please.

    opened by YuGong123 0
Owner
Daniel Roich
Daniel Roich
GitHub
source code for https://arxiv.org/abs/2005.11248 "Accelerating Antimicrobial Discovery with Controllable Deep Generative Models and Molecular Dynamics"

Accelerating Antimicrobial Discovery with Controllable Deep Generative Models and Molecular Dynamics This work will be published in Nature Biomedical

International Business Machines 71 Nov 15, 2022
Official Implementation for "ReStyle: A Residual-Based StyleGAN Encoder via Iterative Refinement" https://arxiv.org/abs/2104.02699

ReStyle: A Residual-Based StyleGAN Encoder via Iterative Refinement Recently, the power of unconditional image synthesis has significantly advanced th

null 967 Jan 4, 2023
We have implemented shaDow-GNN as a general and powerful pipeline for graph representation learning. For more details, please find our paper titled Deep Graph Neural Networks with Shallow Subgraph Samplers, available on arXiv (https//arxiv.org/abs/2012.01380).

Deep GNN, Shallow Sampling Hanqing Zeng, Muhan Zhang, Yinglong Xia, Ajitesh Srivastava, Andrey Malevich, Rajgopal Kannan, Viktor Prasanna, Long Jin, R

Facebook Research 117 Dec 20, 2022
[PyTorch] Official implementation of CVPR2021 paper "PointDSC: Robust Point Cloud Registration using Deep Spatial Consistency". https://arxiv.org/abs/2103.05465

PointDSC repository PyTorch implementation of PointDSC for CVPR'2021 paper "PointDSC: Robust Point Cloud Registration using Deep Spatial Consistency",

null 153 Dec 14, 2022
Official implementation of the paper Image Generators with Conditionally-Independent Pixel Synthesis https://arxiv.org/abs/2011.13775

CIPS -- Official Pytorch Implementation of the paper Image Generators with Conditionally-Independent Pixel Synthesis Requirements pip install -r requi

Multimodal Lab @ Samsung AI Center Moscow 201 Dec 21, 2022
This is an official implementation of our CVPR 2021 paper "Bottom-Up Human Pose Estimation Via Disentangled Keypoint Regression" (https://arxiv.org/abs/2104.02300)

Bottom-Up Human Pose Estimation Via Disentangled Keypoint Regression Introduction In this paper, we are interested in the bottom-up paradigm of estima

HRNet 367 Dec 27, 2022
Pytorch implementation of Each Part Matters: Local Patterns Facilitate Cross-view Geo-localization https://arxiv.org/abs/2008.11646

[TCSVT] Each Part Matters: Local Patterns Facilitate Cross-view Geo-localization LPN [Paper] NEWs Prerequisites Python 3.6 GPU Memory >= 8G Numpy > 1.

null 46 Dec 14, 2022
A PyTorch implementation of EventProp [https://arxiv.org/abs/2009.08378], a method to train Spiking Neural Networks

Spiking Neural Network training with EventProp This is an unofficial PyTorch implemenation of EventProp, a method to compute exact gradients for Spiki

Pedro Savarese 35 Jul 29, 2022
Unofficial implementation of Alias-Free Generative Adversarial Networks. (https://arxiv.org/abs/2106.12423) in PyTorch

alias-free-gan-pytorch Unofficial implementation of Alias-Free Generative Adversarial Networks. (https://arxiv.org/abs/2106.12423) This implementation

Kim Seonghyeon 502 Jan 3, 2023
Pytorch implementation of Distributed Proximal Policy Optimization: https://arxiv.org/abs/1707.02286

Pytorch-DPPO Pytorch implementation of Distributed Proximal Policy Optimization: https://arxiv.org/abs/1707.02286 Using PPO with clip loss (from https

Alexis David Jacq 163 Dec 26, 2022