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
A tiny, pedagogical neural network library with a pytorch-like API.

candl A tiny, pedagogical implementation of a neural network library with a pytorch-like API. The primary use of this library is for education. Use th

Sri Pranav 3 May 23, 2022
Using the provided dataset which includes various book features, in order to predict the price of books, using various proposed methods and models.

Using the provided dataset which includes various book features, in order to predict the price of books, using various proposed methods and models.

Nikolas Petrou 1 Jan 13, 2022
High-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently.

TL;DR Ignite is a high-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently. Click on the image to

4.2k Jan 01, 2023
Official Pytorch implementation of the paper "MotionCLIP: Exposing Human Motion Generation to CLIP Space"

MotionCLIP Official Pytorch implementation of the paper "MotionCLIP: Exposing Human Motion Generation to CLIP Space". Please visit our webpage for mor

Guy Tevet 173 Dec 26, 2022
State of the art Semantic Sentence Embeddings

Contrastive Tension State of the art Semantic Sentence Embeddings Published Paper · Huggingface Models · Report Bug Overview This is the official code

Fredrik Carlsson 88 Dec 30, 2022
GBK-GNN: Gated Bi-Kernel Graph Neural Networks for Modeling Both Homophily and Heterophily

GBK-GNN: Gated Bi-Kernel Graph Neural Networks for Modeling Both Homophily and Heterophily Abstract Graph Neural Networks (GNNs) are widely used on a

10 Dec 20, 2022
Use .csv files to record, play and evaluate motion capture data.

Purpose These scripts allow you to record mocap data to, and play from .csv files. This approach facilitates parsing of body movement data in statisti

21 Dec 12, 2022
MODALS: Modality-agnostic Automated Data Augmentation in the Latent Space

Update (20 Jan 2020): MODALS on text data is avialable MODALS MODALS: Modality-agnostic Automated Data Augmentation in the Latent Space Table of Conte

38 Dec 15, 2022
PyTorch implementation of Octave Convolution with pre-trained Oct-ResNet and Oct-MobileNet models

octconv.pytorch PyTorch implementation of Octave Convolution in Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octa

Duo Li 273 Dec 18, 2022
Code for the paper "TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks"

TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks This is a Python3 / Pytorch implementation of TadGAN paper. The associated

Arun 92 Dec 03, 2022
Code for IntraQ, PyTorch implementation of our paper under review

IntraQ: Learning Synthetic Images with Intra-Class Heterogeneity for Zero-Shot Network Quantization paper Requirements Python = 3.7.10 Pytorch == 1.7

1 Nov 19, 2021
Re-implementation of the vector capsule with dynamic routing

VectorCapsule Re-implementation of the vector capsule with dynamic routing We implement the vector capsule and dynamic routing via graph neural networ

ZhenchaoTang 10 Feb 10, 2022
An unopinionated replacement for PyTorch's Dataset and ImageFolder, that handles Tar archives

Simple Tar Dataset An unopinionated replacement for PyTorch's Dataset and ImageFolder classes, for datasets stored as uncompressed Tar archives. Just

Joao Henriques 47 Dec 20, 2022
Informal Persian Universal Dependency Treebank

Informal Persian Universal Dependency Treebank (iPerUDT) Informal Persian Universal Dependency Treebank, consisting of 3000 sentences and 54,904 token

Roya Kabiri 0 Jan 05, 2022
RCD: Relation Map Driven Cognitive Diagnosis for Intelligent Education Systems

RCD: Relation Map Driven Cognitive Diagnosis for Intelligent Education Systems This is our implementation for the paper: Weibo Gao, Qi Liu*, Zhenya Hu

BigData Lab @USTC 中科大大数据实验室 10 Oct 16, 2022
Code Impementation for "Mold into a Graph: Efficient Bayesian Optimization over Mixed Spaces"

Code Impementation for "Mold into a Graph: Efficient Bayesian Optimization over Mixed Spaces" This repo contains the implementation of GEBO algorithm.

Jaeyeon Ahn 2 Mar 22, 2022
Use your Philips Hue lights as Racing Flags. Works with Assetto Corsa, Assetto Corsa Competizione and iRacing.

phue-racing-flags Use your Philips Hue lights as Racing Flags. Explore the docs » Report Bug · Request Feature Table of Contents About The Project Bui

50 Sep 03, 2022
Official implementation of the RAVE model: a Realtime Audio Variational autoEncoder

Official implementation of the RAVE model: a Realtime Audio Variational autoEncoder

Antoine Caillon 589 Jan 02, 2023
Supervised forecasting of sequential data in Python.

Supervised forecasting of sequential data in Python. Intro Supervised forecasting is the machine learning task of making predictions for sequential da

The Alan Turing Institute 54 Nov 15, 2022
Source code and dataset of the paper "Contrastive Adaptive Propagation Graph Neural Networks forEfficient Graph Learning"

CAPGNN Source code and dataset of the paper "Contrastive Adaptive Propagation Graph Neural Networks forEfficient Graph Learning" Paper URL: https://ar

1 Mar 12, 2022