Projects sharing features with Adversarial

This project is a collection of PyTorch deep learning courseware consisting of practical projects and programming exercises. It focuses on implementing neural network architectures and model training to solve complex data problems. The repository includes a computer vision project suite for building image classifiers, autoencoders, and style transfer applications. It features a generative adversarial network lab for creating synthetic images and specific implementations for transfer learning to adapt pre-trained weights to new tasks. The codebase covers sequential data analysis for natural l

CycleGAN is a generative adversarial network framework designed for unpaired image-to-image translation. It enables the conversion of images between two distinct visual domains using datasets that do not require direct one-to-one matching examples. The project implements a deep learning style transfer tool capable of artistic style transfer, object transfiguration, and domain-to-domain conversion. It uses a dual-generator architecture and cycle-consistency loss to ensure that images translated to a target domain and back recover their original state. The framework covers core machine learnin

StyleGAN2 is a TensorFlow generative adversarial network and image synthesis model designed to produce high-resolution synthetic visual content. It functions as a deep learning architecture that learns patterns from image datasets to synthesize new images. The project includes a latent space projection tool for mapping existing images to latent vectors to analyze their representation within a generative model. It also provides an image quality evaluation framework to measure the visual fidelity and diversity of synthetic outputs. The system covers the full generative pipeline, including imag

This project provides a comprehensive educational curriculum and research resource for deep learning, focusing on the theoretical and technical foundations of neural network implementation. It serves as a structured academic guide for building and training complex models from scratch, covering the essential mathematical primitives, computational graph construction, and automatic differentiation mechanisms required for modern machine learning. The repository distinguishes itself through its extensive coverage of generative modeling and specialized neural architectures. It includes practical im

Code for the paper "Improved Techniques for Training GANs"

Pytorch implementation

This project is a diffusion model training framework and image synthesis pipeline. It provides the tools necessary to train generative models to learn image data distributions through an iterative denoising process. The framework includes a generative model evaluation tool consisting of automated scripts used to measure the quality and accuracy of produced samples. The system covers model training pipelines and performance evaluation for generative diffusion models.

This project is a PyTorch implementation of StyleGAN2, providing a library and research framework for training style-based generative adversarial networks. It serves as a toolkit for high-resolution image synthesis, utilizing competitive minimax optimization to create realistic synthetic visual content. The framework incorporates specialized architectural components such as style-based latent mapping, multi-scale feature modulation, and self-attention layers to improve structural coherence. It distinguishes itself with advanced training stability techniques, including exponential moving avera

Srez is a deep learning image super-resolution framework designed to upscale low-resolution images into sharp, high-resolution visual features. It functions as a neural network training tool that employs generative adversarial networks to synthesize realistic image details. The project includes a model evolution visualizer that generates animations and image batches to track visual improvements during the training process. It utilizes a combination of adversarial and L1 loss functions to optimize model weights and supports periodic state checkpointing for recovery and deployment. The system

AnimeGAN is a generative adversarial network and image translator developed with TensorFlow. It is designed for photo-to-anime style transfer, utilizing a deep learning system to transform real-world photographs and video frames into anime-style imagery. The system includes a video-to-anime converter that applies consistent visual transformations across sequential frames. It supports both the training of generative networks on artistic datasets to replicate specific styles and the extraction of generator weights from checkpoints for efficient inference. The project provides utilities for ima

OUCML is a machine learning research curator and automated data curation tool. It provides a collection of structured research papers, code samples, and study guides designed for mastering complex data science concepts. The project includes a generative adversarial network training framework that uses generator and discriminator models to iteratively refine synthetic data. It also functions as a tensor-based computation library for performing high-dimensional matrix operations to accelerate neural network training. The system covers machine learning education and research curation by aggrega

UGATIT is an unsupervised generative adversarial network and image-to-image translation model implemented in TensorFlow. It serves as the official research implementation of an ICLR 2020 paper, providing a framework for converting images between different visual styles without requiring paired training examples. The system utilizes an unsupervised generative attentional network and attention maps to deform geometric shapes and modify textures during the translation process. It employs a cycle-consistent framework to ensure translation quality by requiring images to return to their original st

This project is a machine learning educational repository providing a collection of implementations and guides for machine learning and deep learning algorithms. It serves as a deep learning model library and a reference for training workflows, covering foundational machine learning, convolutional, recurrent, and transformer architectures. The collection includes a generative adversarial network suite for synthesizing realistic images and performing image-to-image translation. It also functions as a computer vision implementation guide for object detection and semantic segmentation, alongside

TecoGAN is a generative adversarial network designed for video super-resolution. It functions as a spatio-temporal video upscaler that increases the resolution of video sequences while reconstructing high-quality imagery from lower-resolution inputs. The system utilizes a temporal coherence framework to ensure visual stability and reduce flickering in generated frames. It achieves this by employing spatio-temporal discriminators that evaluate both individual frame quality and movement consistency. The project covers the training and optimization of generative adversarial networks, specifical

Awesome-GANs is a curated resource list and research repository focused on the development and evaluation of generative adversarial networks. It serves as a structured index for academic literature and open-source implementations dedicated to the creation of synthetic data generators. The project provides a framework for training competing neural networks to produce outputs that mimic the statistical properties of original datasets. It emphasizes the use of configuration-driven pipelines to manage model hyperparameters and dataset paths, facilitating reproducible research workflows and standa

This project is a deep learning library built for single-image super-resolution and visual enhancement. It provides a framework for training and deploying neural network architectures designed to reconstruct high-resolution images from low-resolution sources, effectively recovering fine details and removing artifacts caused by downscaling or compression. The library distinguishes itself through the implementation of generative adversarial networks and residual block architectures, which work together to improve the realism and clarity of upscaled outputs. It supports training through both pix

This repository is a collection of guided tutorials for building and training machine learning models using the TensorFlow framework. It provides practical walkthroughs and examples for implementing a variety of model architectures to solve data prediction and analysis problems. The guides cover the construction of feedforward, convolutional, and recurrent neural networks to analyze complex data patterns. It includes specific tutorials for unsupervised learning, such as denoising autoencoders and word-to-vec embeddings, as well as examples for training generative adversarial networks to synth

This project is a PyTorch-based generative framework and implementation template for building Generative Adversarial Networks. It provides a collection of foundational toolkits and architectural patterns designed to synthesize high-quality artificial data while focusing on the stability of adversarial neural networks. The framework distinguishes itself through a specialized toolkit for conditional image generation, which integrates discrete labels and auxiliary classification into the training process. It utilizes specific mechanisms to guide the generative process toward target classes by co

This project is a deep learning framework designed for training and deploying image-to-image translation models. It serves as a research platform for experimenting with neural network architectures that transform visual content between distinct stylistic domains, supporting both paired and unpaired training data. The framework distinguishes itself through its support for cycle-consistency constraints, which allow for image translation between domains without requiring corresponding paired examples. It provides a structured pipeline that utilizes adversarial loss optimization, where generator

This is a library of generative model architectures built using the TensorFlow framework. It provides implementations for producing synthetic data and realistic images, specifically focusing on Variational Autoencoders and various Generative Adversarial Network variants. The collection includes specific GAN architectures such as WGAN-GP, LSGAN, InfoGAN, and EBGAN. It also features Variational Autoencoders designed to learn latent representations and synthesize new samples from learned distributions. The project covers image processing pipelines for normalizing and cropping data, as well as a

PyTorch-GAN is a research-oriented framework providing a collection of modular implementations for generative adversarial network architectures. It serves as a toolkit for training and evaluating models that utilize adversarial minimax optimization to produce synthetic data, offering a structured environment for exploring complex generative tasks within the PyTorch ecosystem. The library distinguishes itself through a comprehensive suite of image synthesis and manipulation capabilities, including super-resolution, inpainting, and cross-domain style translation. It supports advanced training m

ESRGAN is a deep learning image restoration framework designed for image super-resolution. It uses a generative adversarial network system to upscale low-resolution images into high-quality versions with sharp visual details and recovered fine textures. The framework implements a perceptual super-resolution model that optimizes the trade-off between perceived visual quality and pixel-level signal-to-noise ratio. It includes weight-interpolation blending to allow for the adjustment of visual sharpness and signal-to-noise ratios by mixing weights from different trained models. The system cover

This project is a PyTorch implementation of a generative adversarial network designed for high-resolution image synthesis. It provides an image synthesis model that produces realistic images from latent vectors and learned class conditions, supported by a latent space projection tool to find numerical vectors representing specific target images. The implementation features adaptive discriminator augmentation, a training technique used to prevent discriminator overfitting when training on limited image datasets. It also includes a generative model evaluation suite providing quantitative metric

This repository is a comprehensive collection of instructional guides and practical examples for Python development, focusing on machine learning, data science, and web scraping. It provides implementations for neural networks, reinforcement learning algorithms, and deep learning architectures using PyTorch, alongside detailed manuals for scientific computing and data visualization. The project distinguishes itself by offering specialized tutorials on concurrent programming to optimize CPU performance and guides for setting up Linux development environments. It covers the implementation of ad

This project is a TensorFlow implementation of an image-to-image translation framework based on conditional generative adversarial networks. It provides the tools to train models that map input images to output images based on learned visual patterns, as well as a server for processing image translation requests and serving trained model checkpoints to web clients. The framework includes a system for converting trained model weights into a portable format for browser-based inference. It also features a validation process that generates comparative reports by analyzing input, output, and targe

MMF is a modular framework for building, training, and evaluating vision-and-language models. It provides a configuration-driven experiment system where model, dataset, and training parameters are defined through composable YAML files, alongside a curated model zoo of pretrained checkpoints for state-of-the-art multimodal architectures. The framework includes a multimodal dataset loader that downloads, processes, and batches vision-and-language data, and a vision-language model trainer supporting distributed training, mixed precision, and checkpoint-based resumption. The framework distinguish

Edward is a probabilistic programming language and inference engine designed for building deep generative models and Bayesian neural networks. It utilizes the TensorFlow framework to represent probabilistic models as differentiable computational graphs. The library enables the construction of complex data distributions through Bayesian neural networks, mixture models, and Gaussian processes. It differentiates itself by providing an integrated toolkit for both supervised and unsupervised probabilistic modeling, including the implementation of generative adversarial networks and mixture density