style mixing for animation face

Last update: Nov 30, 2022

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Overview

An implementation of StyleGAN on Animation dataset.

Install

git clone https://github.com/MorvanZhou/anime-StyleGAN
cd anime-StyleGAN
pip install -r requirements.txt

Process data

Download data from kaggle Anime Faces (~400MB), then unzip *.zip to the anime/images folder.

process data to tensorflow tensor_record format.

python dataset.py --data_dir ~/data/anime/

Train

python train.py --data_dir ~/data/anime/ --soft_gpu -b 32 --latent 128 --epoch 151 -lr 0.0005 -b1 0. -b2 0.99 -w 2

Results

First few of epoch:

After one day:

Style mixing

python restore.py --model_path visual/2021-01-14_19-39-20/model/cp-0137.ckpt -o demo/stylemix.png -n 3 --z_seed 2 --n_seed 1 --latent 128

Owner

Morvan

Deep Learning Research & Development in Tencent

GitHub Repository

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style mixing for animation face

Related tags

Overview

Install

Process data

Train

Results

Style mixing

Owner

Morvan

Julia package for contraction of tensor networks, based on the sweep line algorithm outlined in the paper General tensor network decoding of 2D Pauli codes

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style mixing for animation face

Related tags

Overview

Install

Process data

Train

Results

Style mixing

Owner

Morvan

Julia package for contraction of tensor networks, based on the sweep line algorithm outlined in the paper General tensor network decoding of 2D Pauli codes

2D&3D human pose estimation

Unsupervised Representation Learning via Neural Activation Coding

A toolkit for making real world machine learning and data analysis applications in C++

This repo is to present various code demos on how to use our Graph4NLP library.

Exploiting Robust Unsupervised Video Person Re-identification

Behind the Curtain: Learning Occluded Shapes for 3D Object Detection

Patch Rotation: A Self-Supervised Auxiliary Task for Robustness and Accuracy of Supervised Models

PyTorch implementation of paper “Unbiased Scene Graph Generation from Biased Training”

Analyzing basic network responses to novel classes

A Dynamic Residual Self-Attention Network for Lightweight Single Image Super-Resolution

SW components and demos for visual kinship recognition. An emphasis is put on the FIW dataset-- data loaders, benchmarks, results in summary.

Speech Separation Using an Asynchronous Fully Recurrent Convolutional Neural Network

Rule-based Customer Segmentation

System Combination for Grammatical Error Correction Based on Integer Programming

code for paper "Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning" by Zhongzheng Ren*, Raymond A. Yeh*, Alexander G. Schwing.

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Everything's Talkin': Pareidolia Face Reenactment (CVPR2021)

code for paper "Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning" by Zhongzheng Ren, Raymond A. Yeh, Alexander G. Schwing.