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spongebob-1703's Projects

clip icon clip

CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image

cvae-gan-zoos-pytorch-beginner icon cvae-gan-zoos-pytorch-beginner

For beginner, this will be the best start for VAEs, GANs, and CVAE-GAN. This contains AE, DAE, VAE, GAN, CGAN, DCGAN, WGAN, WGAN-GP, VAE-GAN, CVAE-GAN. All use PyTorch.

cvpr2016 icon cvpr2016

Learning Deep Representations of Fine-grained Visual Descriptions

dualgan icon dualgan

DualGAN-tensorflow: tensorflow implementation of DualGAN

gan icon gan

Resources and Implementations of Generative Adversarial Nets: GAN, DCGAN, WGAN, CGAN, InfoGAN

ganhacks icon ganhacks

starter from "How to Train a GAN?" at NIPS2016

icml2016 icon icml2016

Generative Adversarial Text-to-Image Synthesis

img2dataset icon img2dataset

Easily turn large sets of image urls to an image dataset. Can download, resize and package 100M urls in 20h on one machine.

improved-gan icon improved-gan

Code for the paper "Improved Techniques for Training GANs"

infogan icon infogan

Code for reproducing key results in the paper "InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets"

jarvis icon jarvis

Personal Voice Assistant made with Python and has a cool looking GUI

paddlegan icon paddlegan

PaddlePaddle GAN library, including lots of interesting applications like First-Order motion transfer, Wav2Lip, picture repair, image editing, photo2cartoon, image style transfer, GPEN, and so on.

passgan icon passgan

A Deep Learning Approach for Password Guessing (https://arxiv.org/abs/1709.00440)

pytorch-gan icon pytorch-gan

PyTorch implementations of Generative Adversarial Networks.

sd2gan icon sd2gan

SD2GAN: A Siamese Dual Discriminator Generative Adversarial Network for Mode Collapse Reduction

siamese-neural-networks-for-one-shot-image-recognition icon siamese-neural-networks-for-one-shot-image-recognition

One-shot Siamese Neural Network, using TensorFlow 2.0, based on the work presented by Gregory Koch, Richard Zemel, and Ruslan Salakhutdinov. we used the “Labeled Faces in the Wild” dataset with over 5,700 different people. Some people have a single image, while others have dozens.

singan icon singan

Implementation of the paper: "SinGAN: Learning a Generative Model from a Single Natural Image"

singan_original icon singan_original

Official pytorch implementation of the paper: "SinGAN: Learning a Generative Model from a Single Natural Image"

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