Simple SN-GAN to generate CryptoPunks

Overview

CryptoPunks GAN

Simple SN-GAN to generate CryptoPunks. Neural network architecture and training code has been modified from the PyTorch DCGAN example. See Notes for more information.

Punks during training

Linear interpolation between two punks

Usage

Example

Generate 64 punks using pretrained model:

import torch
from torchvision.utils import save_image
from train import Generator

model = Generator()
model.load_state_dict(torch.load("models/net_g_epoch_999.pth"))
z = torch.randn(64, 100, 1, 1)
punks = model(z)
save_image(punks, "punks.png", normalize=True)

Train

On CUDA machine:

python train.py

Check out directory for weights and sample images.

Notes

Using Spectral Normalization created very nice punks, but had a tendency to create very transparent ones:

Using batch normalization seems to negate this, but causes mode collapse

Instead, penalizing the model for a high mean alpha produces very good punks, with no transparency issues:

# minimize criterion + mean of alpha channel
loss_g = criterion(output, label) + fake[:, -1].mean()

In order to encourage more variety in the outputs, we generate a feature matrix of all the punks (using punks.attributes):

    3D Glasses  Bandana  Beanie  Big Beard  Big Shades  ...  Ape  Human  Zombie  Female  Male
id                                                      ...
0            0        0       0          0           0  ...    0      1       0       1     0
1            0        0       0          0           0  ...    0      1       0       0     1
2            0        0       0          0           0  ...    0      1       0       1     0
3            0        0       0          0           0  ...    0      1       0       0     1
4            0        0       0          0           1  ...    0      1       0       0     1

For each image in the batch, randomly select an attribute (column), and then randomly select a punk that has that attribute. This ensures that the discriminator is exposed to all of the attributes in a more balanced manor than running through all the punks.

Owner
Teddy Koker
Machine Learning @mit-ll
Teddy Koker
Pyramid Scene Parsing Network, CVPR2017.

Pyramid Scene Parsing Network by Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, Jiaya Jia, details are in project page. Introduction This

Hengshuang Zhao 1.5k Jan 05, 2023
Optimized primitives for collective multi-GPU communication

NCCL Optimized primitives for inter-GPU communication. Introduction NCCL (pronounced "Nickel") is a stand-alone library of standard communication rout

NVIDIA Corporation 2k Jan 09, 2023
[Preprint] "Chasing Sparsity in Vision Transformers: An End-to-End Exploration" by Tianlong Chen, Yu Cheng, Zhe Gan, Lu Yuan, Lei Zhang, Zhangyang Wang

Chasing Sparsity in Vision Transformers: An End-to-End Exploration Codes for [Preprint] Chasing Sparsity in Vision Transformers: An End-to-End Explora

VITA 64 Dec 08, 2022
GPT-Code-Clippy (GPT-CC) is an open source version of GitHub Copilot

GPT-Code-Clippy (GPT-CC) is an open source version of GitHub Copilot, a language model -- based on GPT-3, called GPT-Codex -- that is fine-tuned on publicly available code from GitHub.

2.3k Jan 09, 2023
Shallow Convolutional Neural Networks for Human Activity Recognition using Wearable Sensors

-IEEE-TIM-2021-1-Shallow-CNN-for-HAR [IEEE TIM 2021-1] Shallow Convolutional Neural Networks for Human Activity Recognition using Wearable Sensors All

Wenbo Huang 1 May 17, 2022
BrainGNN - A deep learning model for data-driven discovery of functional connectivity

A deep learning model for data-driven discovery of functional connectivity https://doi.org/10.3390/a14030075 Usman Mahmood, Zengin Fu, Vince D. Calhou

Usman Mahmood 3 Aug 28, 2022
Extension to fastai for volumetric medical data

FAIMED 3D use fastai to quickly train fully three-dimensional models on radiological data Classification from faimed3d.all import * Load data in vari

Keno 26 Aug 22, 2022
Variational Attention: Propagating Domain-Specific Knowledge for Multi-Domain Learning in Crowd Counting (ICCV, 2021)

DKPNet ICCV 2021 Variational Attention: Propagating Domain-Specific Knowledge for Multi-Domain Learning in Crowd Counting Baseline of DKPNet is availa

19 Oct 14, 2022
3D-Reconstruction 基于深度学习方法的单目多视图三维重建

基于深度学习方法的单目多视图三维重建 Part I 三维重建 代码:Part1 技术文档:[Markdown] [PDF] 原始图像:Original Images 点云结果:Point Cloud Results-1

HMT_Curo 19 Dec 26, 2022
Keras implementation of Normalizer-Free Networks and SGD - Adaptive Gradient Clipping

Keras implementation of Normalizer-Free Networks and SGD - Adaptive Gradient Clipping

Yam Peleg 63 Sep 21, 2022
This is the research repository for Vid2Doppler: Synthesizing Doppler Radar Data from Videos for Training Privacy-Preserving Activity Recognition.

Vid2Doppler: Synthesizing Doppler Radar Data from Videos for Training Privacy-Preserving Activity Recognition This is the research repository for Vid2

Future Interfaces Group (CMU) 26 Dec 24, 2022
Model Zoo for MindSpore

Welcome to the Model Zoo for MindSpore In order to facilitate developers to enjoy the benefits of MindSpore framework, we will continue to add typical

MindSpore 226 Jan 07, 2023
The official repo for CVPR2021——ViPNAS: Efficient Video Pose Estimation via Neural Architecture Search.

ViPNAS: Efficient Video Pose Estimation via Neural Architecture Search [paper] Introduction This is the official implementation of ViPNAS: Efficient V

Lumin 42 Sep 26, 2022
Implementation of Cross Transformer for spatially-aware few-shot transfer, in Pytorch

Cross Transformers - Pytorch (wip) Implementation of Cross Transformer for spatially-aware few-shot transfer, in Pytorch Install $ pip install cross-t

Phil Wang 40 Dec 22, 2022
Referring Video Object Segmentation

Awesome-Referring-Video-Object-Segmentation Welcome to starts ⭐ & comments 💹 & sharing 😀 !! - 2021.12.12: Recent papers (from 2021) - welcome to ad

Explorer 57 Dec 11, 2022
DANet for Tabular data classification/ regression.

Deep Abstract Networks A PyTorch code implemented for the submission DANets: Deep Abstract Networks for Tabular Data Classification and Regression. Do

Ronnie Rocket 55 Sep 14, 2022
Reusable constraint types to use with typing.Annotated

annotated-types PEP-593 added typing.Annotated as a way of adding context-specific metadata to existing types, and specifies that Annotated[T, x] shou

125 Dec 26, 2022
Object-aware Contrastive Learning for Debiased Scene Representation

Object-aware Contrastive Learning Official PyTorch implementation of "Object-aware Contrastive Learning for Debiased Scene Representation" by Sangwoo

43 Dec 14, 2022
Deep Learning Head Pose Estimation using PyTorch.

Hopenet is an accurate and easy to use head pose estimation network. Models have been trained on the 300W-LP dataset and have been tested on real data with good qualitative performance.

Nataniel Ruiz 1.3k Dec 26, 2022
Computational inteligence project on faces in the wild dataset

Table of Contents The general idea How these scripts work? Loading data Needed modules and global variables Parsing the arrays in dataset Extracting a

tooraj taraz 4 Oct 21, 2022