SimGAN-Captcha - Solve captcha without manually labeling a training set

  •        51

With simulated unsupervised learning, breaking captchas has never been easier. There is no need to label any captchas manually for convnet. By using a captcha synthesizer and a refiner trained with GAN, it's feasible to generate synthesized training pairs for classifying captchas. HackMIT Puzzle #5.

https://github.com/rickyhan/SimGAN-Captcha

Tags
Implementation
License
Platform

   




Related Projects

Keras-GAN - Keras implementations of Generative Adversarial Networks.

  •    Python

Collection of Keras implementations of Generative Adversarial Networks (GANs) suggested in research papers. These models are in some cases simplified versions of the ones ultimately described in the papers, but I have chosen to focus on getting the core ideas covered instead of getting every layer configuration right. Contributions and suggestions of GAN varieties to implement are very welcomed. Implementation of Auxiliary Classifier Generative Adversarial Network.

gan-playground - GAN Playground - Experiment with Generative Adversarial Nets in your browser

  •    TypeScript

GAN Playground lets you play around with Generative Adversarial Networks right in your browser. Currently, it contains three built-in datasets: MNIST, Fashion MNIST, and CIFAR-10. GAN Playground provides you the ability to set your models' hyperparameters and build up your discriminator and generator layer-by-layer. You can observe the network learn in real time as the generator produces more and more realistic images, or more likely, gets stuck in failure modes such as mode collapse.

All-About-the-GAN - All About the GANs(Generative Adversarial Networks) - Summarized lists for GAN

  •    Python

The purpose of this repository is providing the curated list of the state-of-the-art works on the field of Generative Adversarial Networks since their introduction in 2014. You can also check out the same data in a tabular format with functionality to filter by year or do a quick search by title here.

PyTorch-Tutorial - Build your neural network easy and fast

  •    Jupyter

In these tutorials for pyTorch, we will build our first Neural Network and try to build some advanced Neural Network architectures developed recent years. Thanks for liufuyang's notebook files which is a great contribution to this tutorial.

DiscoGAN-pytorch - PyTorch implementation of "Learning to Discover Cross-Domain Relations with Generative Adversarial Networks"

  •    Jupyter

PyTorch implementation of Learning to Discover Cross-Domain Relations with Generative Adversarial Networks. * All samples in README.md are genearted by neural network except the first image for each row. * Network structure is slightly diffferent (here) from the author's code.


Tensorflow-Tutorial - Tensorflow tutorial from basic to hard

  •    Python

In these tutorials, we will build our first Neural Network and try to build some advanced Neural Network architectures developed recent years. All methods mentioned below have their video and text tutorial in Chinese. Visit 莫烦 Python for more.

StarGAN - Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation

  •    Python

PyTorch implementation of StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation. StarGAN can flexibly translate an input image to any desired target domain using only a single generator and a discriminator.

generative-compression - TensorFlow Implementation of Generative Adversarial Networks for Extreme Learned Image Compression

  •    Python

TensorFlow Implementation for learned compression of images using Generative Adversarial Networks. The method was developed by Agustsson et. al. in Generative Adversarial Networks for Extreme Learned Image Compression. The proposed idea is very interesting and their approach is well-described. Training is conducted with batch size 1 and reconstructed samples / tensorboard summaries will be periodically written every certain number of steps (default is 128). Checkpoints are saved every 10 epochs.

Super-Resolution-using-Generative-Adversarial-Networks - An implementation of SRGAN model in Keras

  •    Python

This is an implementation of the SRGAN model proposed in the paper Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network in Keras. Note that this project is a work in progress. The SRGAN model is built in stages within models.py. Initially, only the SR-ResNet model is created, to which the VGG network is appended to create the pre-training model. The VGG weights are freezed as we will not update these weights.

faceswap-GAN - A denoising autoencoder + adversarial losses and attention mechanisms for face swapping

  •    Jupyter

Adding Adversarial loss and perceptual loss (VGGface) to deepfakes'(reddit user) auto-encoder architecture. Here is a playground notebook for faceswap-GAN v2.2 on Google Colab. Users can train their own model in the browser without GPU required.

PyTorch-GAN - PyTorch implementations of Generative Adversarial Networks.

  •    Python

Collection of PyTorch implementations of Generative Adversarial Network varieties presented in research papers. Model architectures will not always mirror the ones proposed in the papers, but I have chosen to focus on getting the core ideas covered instead of getting every layer configuration right. Contributions and suggestions of GANs to implement are very welcomed. Synthesizing high resolution photorealistic images has been a long-standing challenge in machine learning. In this paper we introduce new methods for the improved training of generative adversarial networks (GANs) for image synthesis. We construct a variant of GANs employing label conditioning that results in 128x128 resolution image samples exhibiting global coherence. We expand on previous work for image quality assessment to provide two new analyses for assessing the discriminability and diversity of samples from class-conditional image synthesis models. These analyses demonstrate that high resolution samples provide class information not present in low resolution samples. Across 1000 ImageNet classes, 128x128 samples are more than twice as discriminable as artificially resized 32x32 samples. In addition, 84.7% of the classes have samples exhibiting diversity comparable to real ImageNet data.

really-awesome-gan - A list of papers on Generative Adversarial (Neural) Networks

  •    

A list of papers and other resources on Generative Adversarial (Neural) Networks. This site is maintained by Holger Caesar. To complement or correct it, please contact me at holger-at-it-caesar.com or visit it-caesar.com. Also checkout really-awesome-semantic-segmentation and our COCO-Stuff dataset. NOTE: Despite the enormous interest in this cite (~3000 visitors per month), I will no longer add new papers starting from November 2017. I feel that GANs have come from an exotic topic to the mainstream and an exhaustive list of all GAN papers is no more feasible or useful. However, I invite other people to continue this effort and reuse my list.

cppn-gan-vae-tensorflow - Train CPPNs as a Generative Model, using Generative Adversarial Networks and Variational Autoencoder techniques to produce high resolution images

  •    Python

Train Compositional Pattern Producing Network as a Generative Model, using Generative Adversarial Networks and Variational Autoencoder techniques to produce high resolution images. Run python train.py from the command line to train from scratch and experiment with different settings.

neural_complete - A neural network trained to help writing neural network code using autocomplete

  •    Python

Neural Complete is autocomplete based on a generative LSTM neural network, trained not only by python code but also on python source code. Ironically, it is trained on files containing keras imports. The result is a neural network trained to help writing neural network code.

ocr - Neural network OCR.

  •    Javascript

Trains a multi-layer perceptron (MLP) neural network to perform optical character recognition (OCR). The training set is automatically generated using a heavily modified version of the captcha-generator node-captcha. Support for the MNIST handwritten digit database has been added recently (see performance section).

T2F - T2F: text to face generation using Deep Learning

  •    Python

Text-to-Face generation using Deep Learning. This project combines two of the recent architectures StackGAN and ProGAN for synthesizing faces from textual descriptions. The project uses Face2Text dataset which contains 400 facial images and textual captions for each of them. The data can be obtained by contacting either the RIVAL group or the authors of the aforementioned paper. The code is present in the implementation/ subdirectory. The implementation is done using the PyTorch framework. So, for running this code, please install PyTorch version 0.4.0 before continuing.

iGAN - Interactive Image Generation via Generative Adversarial Networks

  •    Python

[Project] [Youtube] [Paper] A research prototype developed by UC Berkeley and Adobe CTL. Latest development: [pix2pix]: Torch implementation for learning a mapping from input images to output images. [CycleGAN]: Torch implementation for learning an image-to-image translation (i.e. pix2pix) without input-output pairs. [pytorch-CycleGAN-and-pix2pix]: PyTorch implementation for both unpaired and paired image-to-image translation.

pix2pixHD - Synthesizing and manipulating 2048x1024 images with conditional GANs

  •    Python

Pytorch implementation of our method for high-resolution (e.g. 2048x1024) photorealistic image-to-image translation. It can be used for turning semantic label maps into photo-realistic images or synthesizing portraits from face label maps. High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs Ting-Chun Wang1, Ming-Yu Liu1, Jun-Yan Zhu2, Andrew Tao1, Jan Kautz1, Bryan Catanzaro1 1NVIDIA Corporation, 2UC Berkeley In arxiv, 2017.

chainer-gan-lib - Chainer implementation of recent GAN variants

  •    Python

This repository collects chainer implementation of state-of-the-art GAN algorithms. These codes are evaluated with the inception score on Cifar-10 dataset. Note that our codes are not faithful re-implementation of the original paper. This implementation has been tested with the following versions.