Code to reproduce the results in the paper "Tensor Component Analysis for Interpreting the Latent Space of GANs".

Overview

Tensor Component Analysis for Interpreting the Latent Space of GANs

[ paper | project page ]

Code to reproduce the results in the paper "Tensor Component Analysis for Interpreting the Latent Space of GANs".

./images/teaser.png

dependencies

Firstly, to install the required packages, please run:

$ pip install -r requirements.txt

Pretrained weights

To replicate the results in the paper, you'll need to first download the pre-trained weights. To do so, simply run this from the command line:

./download_weights.sh

Quantitative results

building the prediction matrices

To reproduce Fig. 5, one can then run the ./quant.ipynb notebook using the pre-computed classification scores (please see this notebook for more details).

manually computing predictions

To call the Microsoft Azure Face API to generate the predictions again from scratch, one can run the shell script in ./quant/classify.sh. Firstly however, you need to generate our synthetic images to classify, which we detail below.

Qualitative results

generating the images

Reproducing the qualitative results (i.e. in Fig. 6) involves generating synthetic faces and 3 edited versions with the 3 attributes of interest (hair colour, yaw, and pitch). To generate these images (which are also used for the quantitative results), simply run:

$ ./generate_quant_edits.sh

mode-wise edits

./images/116-blonde.gif ./images/116-yaw.gif ./images/116-pitch.gif

Manual edits along individual modes of the tensor are made by calling main.py with the --mode edit_modewise flag. For example, one can reproduce the images from Fig. 3 with:

$ python main.py --cp_rank 0 --tucker_ranks "4,4,4,512" --model_name pggan_celebahq1024 --penalty_lam 0.001 --resume_iters 1000
  --n_to_edit 10 \
  --mode edit_modewise \
  --attribute_to_edit male

multilinear edits

./images/thick.gif

Edits achieved with the 'multilinear mixing' are achieved instead by loading the relevant weights and supplying the --mode edit_multilinear flag. For example, the images in Fig. 4 are generated with:

$ python main.py --cp_rank 0 --tucker_ranks "256,4,4,512" --model_name pggan_celebahq1024 --penalty_lam 0.001 --resume_iters 200000
  --n_to_edit 10 \
  --mode edit_multilinear \
  --attribute_to_edit thick

Please feel free to get in touch at: [email protected], where x=oldfield


credits

All the code in ./architectures/ and utils.py is directly imported from https://github.com/genforce/genforce, only lightly modified to support performing the forward pass through the models partially, and returning the intermediate tensors.

The structure of the codebase follows https://github.com/yunjey/stargan, and hence we use their code as a template to build off. For this reason, you will find small helper functions (e.g. the first few lines of main.py) are borrowed from the StarGAN codebase.

Owner
James Oldfield
James Oldfield
Python script to download the celebA-HQ dataset from google drive

download-celebA-HQ Python script to download and create the celebA-HQ dataset. WARNING from the author. I believe this script is broken since a few mo

133 Dec 21, 2022
Source code for ZePHyR: Zero-shot Pose Hypothesis Rating @ ICRA 2021

ZePHyR: Zero-shot Pose Hypothesis Rating ZePHyR is a zero-shot 6D object pose estimation pipeline. The core is a learned scoring function that compare

R-Pad - Robots Perceiving and Doing 18 Aug 22, 2022
Generic template to bootstrap your PyTorch project with PyTorch Lightning, Hydra, W&B, and DVC.

NN Template Generic template to bootstrap your PyTorch project. Click on Use this Template and avoid writing boilerplate code for: PyTorch Lightning,

Luca Moschella 520 Dec 30, 2022
Implementation of Artificial Neural Network Algorithm

Artificial Neural Network This repository contain implementation of Artificial Neural Network Algorithm in several programming languanges and framewor

Resha Dwika Hefni Al-Fahsi 1 Sep 14, 2022
BiSeNet based on pytorch

BiSeNet BiSeNet based on pytorch 0.4.1 and python 3.6 Dataset Download CamVid dataset from Google Drive or Baidu Yun(6xw4). Pretrained model Download

367 Dec 26, 2022
A dead simple python wrapper for darknet that works with OpenCV 4.1, CUDA 10.1

What Dead simple python wrapper for Yolo V3 using AlexyAB's darknet fork. Works with CUDA 10.1 and OpenCV 4.1 or later (I use OpenCV master as of Jun

Pliable Pixels 6 Jan 12, 2022
A face dataset generator with out-of-focus blur detection and dynamic interval adjustment.

A face dataset generator with out-of-focus blur detection and dynamic interval adjustment.

Yutian Liu 2 Jan 29, 2022
QuickAI is a Python library that makes it extremely easy to experiment with state-of-the-art Machine Learning models.

QuickAI is a Python library that makes it extremely easy to experiment with state-of-the-art Machine Learning models.

152 Jan 02, 2023
Deconfounding Temporal Autoencoder: Estimating Treatment Effects over Time Using Noisy Proxies

Deconfounding Temporal Autoencoder (DTA) This is a repository for the paper "Deconfounding Temporal Autoencoder: Estimating Treatment Effects over Tim

Milan Kuzmanovic 3 Feb 04, 2022
LEAP: Learning Articulated Occupancy of People

LEAP: Learning Articulated Occupancy of People Paper | Video | Project Page This is the official implementation of the CVPR 2021 submission LEAP: Lear

Neural Bodies 60 Nov 18, 2022
Implementation of Hourglass Transformer, in Pytorch, from Google and OpenAI

Hourglass Transformer - Pytorch (wip) Implementation of Hourglass Transformer, in Pytorch. It will also contain some of my own ideas about how to make

Phil Wang 61 Dec 25, 2022
Flexible Option Learning - NeurIPS 2021

Flexible Option Learning This repository contains code for the paper Flexible Option Learning presented as a Spotlight at NeurIPS 2021. The implementa

Martin Klissarov 7 Nov 09, 2022
This python-based package offers a way of creating a parametric OpenMC plasma source from plasma parameters.

openmc-plasma-source This python-based package offers a way of creating a parametric OpenMC plasma source from plasma parameters. The OpenMC sources a

Fusion Energy 10 Oct 18, 2022
Official pytorch implementation of the paper: "SinGAN: Learning a Generative Model from a Single Natural Image"

SinGAN Project | Arxiv | CVF | Supplementary materials | Talk (ICCV`19) Official pytorch implementation of the paper: "SinGAN: Learning a Generative M

Tamar Rott Shaham 3.2k Dec 25, 2022
Code for the prototype tool in our paper "CoProtector: Protect Open-Source Code against Unauthorized Training Usage with Data Poisoning".

CoProtector Code for the prototype tool in our paper "CoProtector: Protect Open-Source Code against Unauthorized Training Usage with Data Poisoning".

Zhensu Sun 1 Oct 26, 2021
Official pytorch implementation for Learning to Listen: Modeling Non-Deterministic Dyadic Facial Motion (CVPR 2022)

Learning to Listen: Modeling Non-Deterministic Dyadic Facial Motion This repository contains a pytorch implementation of "Learning to Listen: Modeling

50 Dec 17, 2022
Employee-Managment - Company employee registration software in the face recognition system

Employee-Managment Company employee registration software in the face recognitio

Alireza Kiaeipour 7 Jul 10, 2022
A denoising diffusion probabilistic model synthesises galaxies that are qualitatively and physically indistinguishable from the real thing.

Realistic galaxy simulation via score-based generative models Official code for 'Realistic galaxy simulation via score-based generative models'. We us

Michael Smith 32 Dec 20, 2022
A modular, open and non-proprietary toolkit for core robotic functionalities by harnessing deep learning

A modular, open and non-proprietary toolkit for core robotic functionalities by harnessing deep learning Website • About • Installation • Using OpenDR

OpenDR 304 Dec 28, 2022
Papers about explainability of GNNs

Papers about explainability of GNNs

Dongsheng Luo 236 Jan 04, 2023