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https://github.com/csinva/gan-vae-pretrained-pytorch

Pretrained GANs + VAEs + classifiers for MNIST/CIFAR in pytorch.

https://github.com/csinva/gan-vae-pretrained-pytorch

Science Score: 33.0%

This score indicates how likely this project is to be science-related based on various indicators:

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    CITATION.cff file
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    codemeta.json file
    Found codemeta.json file
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    .zenodo.json file
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    Academic publication links
    Links to: arxiv.org
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    1 of 2 committers (50.0%) from academic institutions
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    Scientific vocabulary similarity
    Low similarity (10.7%) to scientific vocabulary

Keywords

ai cifar cnn convolutional-neural-networks dcgan deep-learning gan gans generative-adversarial-network generative-adversarial-networks machine-learning ml mnist neural-network pretrained pretrained-models python pytorch pytorch-implementation statistics
Last synced: 5 months ago · JSON representation

Repository

Pretrained GANs + VAEs + classifiers for MNIST/CIFAR in pytorch.

Basic Info
  • Host: GitHub
  • Owner: csinva
  • Language: Jupyter Notebook
  • Default Branch: master
  • Homepage:
  • Size: 98.4 MB
Statistics
  • Stars: 192
  • Watchers: 3
  • Forks: 49
  • Open Issues: 2
  • Releases: 1
Topics
ai cifar cnn convolutional-neural-networks dcgan deep-learning gan gans generative-adversarial-network generative-adversarial-networks machine-learning ml mnist neural-network pretrained pretrained-models python pytorch pytorch-implementation statistics
Created about 7 years ago · Last pushed about 1 year ago
Metadata Files
Readme

readme.md

Pre-trained GANs, VAEs + classifiers for MNIST / CIFAR10

A simple starting point for modeling with GANs/VAEs in pytorch.

  • includes model class definitions + training scripts
  • includes notebooks showing how to load pretrained nets / use them
  • tested with pytorch 1.0+
  • generates images the same size as the dataset images

mnist

Generates images the size of the MNIST dataset (28x28), using an architecture based on the DCGAN paper. Trained for 100 epochs. Weights here.

| data samples | dcgan samples | vae samples | | ------------------------------------------------------------ | ------------------------------------------------------------ | ---------------------------------------------------- | | real_images | fake_images-300 | fake_images-300 |

For comparison with a less complicated architecture, I've also included a pre-trained non-convolutional GAN in the mnistganmlp folder, based on code from this repo (trained for 300 epochs).

I've also included a pre-trained LeNet classifier which achieves 99% test accuracy in the mnist_classifier folder, based on this repo.

cifar10

The cifar10 gan is from the pytorch examples repo and implements the DCGAN paper. It required only minor alterations to generate images the size of the cifar10 dataset (32x32x3). Trained for 200 epochs. Weights here.

| data samples | generated samples | | ------------------------------------------------------------ | ------------------------------------------------------ | | real_images | fake_images-300 |

I've also linked to a pre-trained cifar10 classifier in the cifar10_classifier folder from this repo.

cifar100

Similiar to the above gans, the cifar100 gan here generates 32x32x1 images for generating grayscale images. Trained for 200 epochs. Weights here. There are also weights/code for generating images which are 34x45x1.

| data samples | generated samples | | ------------------------------------------------------------ | ------------------------------------------------------ | | real_images | fake_images-300 |

reference

Owner

  • Name: Chandan Singh
  • Login: csinva
  • Kind: user
  • Location: Microsoft research
  • Company: Senior researcher

Senior researcher @Microsoft interpreting ML models in science and medicine. PhD from UC Berkeley.

GitHub Events

Total
  • Watch event: 20
  • Push event: 1
  • Pull request event: 2
  • Fork event: 6
Last Year
  • Watch event: 20
  • Push event: 1
  • Pull request event: 2
  • Fork event: 6

Committers

Last synced: 9 months ago

All Time
  • Total Commits: 34
  • Total Committers: 2
  • Avg Commits per committer: 17.0
  • Development Distribution Score (DDS): 0.029
Past Year
  • Commits: 1
  • Committers: 1
  • Avg Commits per committer: 1.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Chandan Singh c****h@b****u 33
Ari a****4@g****m 1
Committer Domains (Top 20 + Academic)
berkeley.edu: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 5
  • Total pull requests: 3
  • Average time to close issues: about 1 month
  • Average time to close pull requests: about 23 hours
  • Total issue authors: 5
  • Total pull request authors: 2
  • Average comments per issue: 0.6
  • Average comments per pull request: 0.0
  • Merged pull requests: 2
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 2
  • Average time to close issues: N/A
  • Average time to close pull requests: 1 day
  • Issue authors: 0
  • Pull request authors: 1
  • Average comments per issue: 0
  • Average comments per pull request: 0.0
  • Merged pull requests: 2
  • Bot issues: 0
  • Bot pull requests: 0
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Top Authors
Issue Authors
  • gordon-lim (1)
  • TanmDL (1)
  • sakh251 (1)
  • fogfork (1)
  • kurtisdavid (1)
Pull Request Authors
  • arifr1234 (2)
  • bk-synth (1)
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