Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.

PyTorch implementation of DCGAN, WGAN-GP and SNGAN.

Collection of free Notes,Courses,Videos,Projects,Articles and Repos Links To learn Machine learning ,Deep learning,Python,SQL,CNN,NLP,GAN,GNN,Transfomers,Flask,Django,and End to End Machine learning Projects

This repository implements all kinds of GAN-models based on tensorflow2.0 keras API including GAN, CGAN, WGAN, WGAN_GP, VAE, CVAE, LSGAN, infoGAN, EBGAN, BEGAN, ACGAN

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A collection of metrics for evaluating GAN models.

Torch implementation of various types of GAN (e.g. DCGAN, ALI, Context-encoder, DiscoGAN, CycleGAN, EBGAN, LSGAN)

A collection of AWESOME things about GAN

GANfolk are AI-generated renderings of fictional people. Each image in the collection was created by a pair of Generative Adversarial Networks (GANs) with names and backstories also created with AI. The GANs were trained using portraits from artists like Renoir, Turner, and Modigliani in addition to open-source, modern photos.

[TIP2019]Gated-GAN: Adversarial Gated Networks for Multi-Collection Style Transfer

Collection of GAN models with Flux

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Collection of GAN models in Pytorch

Collection of Deep Learning Jupyter Notebooks. Each notebook is self-contained and presents single architecture. These include MLPs, CNNs, RNNs, Seq2Seq, GANs.

Collections of generative model(VAE, CVAE, GAN, DCGAN)

A collection of highly customizable GANs implemented in PyTorch.

Collection of GAN Algorithms

Collection of generative models, e.g. GAN, VAE in NNabla

Collection of simple examples in modified form, e.g. more stable GAN variants; All in ONE Notebook without many dependencies, no bells and whistles - just clean code examples

In this project, we proposed to build a generative adversarial network (GAN) that could generate point clouds for 3D printing based on 2D image collections. Required input for our network would be 3 images of a household item from different views and a point cloud that could be transformed to STL files containing 3D models was expected to be the output.

A collection of hands-on projects exploring deep learning concepts using TensorFlow and Keras. Each project demonstrates core techniques (e.g., CNNs, RNNs, Transformers, autoencoders, GANs, etc.) with clean, well-documented code and datasets for learning and experimentation.

The human hand plays a crucial role in conveying emotions and carrying out most day-to-day activities. Therefore numerous modern technologies - ranging from gesture control to autonomous driving - would benefit from the reliable recognition of certain hand actions. This can be done using a two-step approach, in which first hand poses are obtained from video frames and then the resulting sequences are classified in the 3D skeleton space. Existing techniques that aim to solve the second step are mostly based on deep learning methods. Given the high complexity and dimensionality of the human hand, these require large amounts of training data to achieve good performance. As the collection of precisely annotated hand pose data is time-consuming and expensive, data augmentation appears as an advantageous practice to increase the recognition accuracy for a given classifier. This thesis proposes a suitable WGAN-GP architecture for the generation of synthetic hand skeleton sequences with variable length. The recommended critic consists of a multi-layer perceptron with three hidden layers, while the generator is based on two RNNs and receives a start frame as input. Both networks are conditioned on the action class. The best performing model was trained on multiple classes simultaneously and selected based on the smallest generator loss. When its synthetic samples were used to augment the training set of a 1-layer LSTM classifier, the classification error on several subsets as well as on the complete dataset decreased. Quantitative results show that the chosen GAN-based data augmentation outperforms alternative standard methods. Furthermore, no clear correlation between the visual appearance of the generated samples and their resulting improvement on recognition accuracy was found.

Collection of GANs created during development.

A Collection of GAN models in Tensorflow

A collection of GAN models for generating synthetic data

Collection of deep learning models featuring CNNs, RNNs, GANs, and much more.

A collection of deep learning projects including classification, object detection, GANs, and transfer learning.

A collection of different GANs (including WGAN-GP, BiGAN and CycleGAN) implemented in PyTorch.

ou'll find a collection of Jupyter Notebook files showcasing various Generative Adversarial Networks (GANs) and their applications. Each notebook provides an interactive and informative environment to explore and experiment with different GAN architectures and techniques.

Collection of RNN GAN SNN CNN in Tensorflow