Projects sharing features with Super Resolution

Pulse is a face image super-resolution tool and self-supervised image enhancer. It functions as a generative model image upsampler and latent space optimization tool designed to increase photo resolution and recover image details. The system differentiates itself by using latent space exploration and spherical constraints to find high-fidelity matches within a generative model. It employs geodesic distance measurement and spherical latent space optimization to regularize representations and maintain parameter radii during the recovery process. The project covers facial image restoration thro

BasicSR is a PyTorch-based image restoration toolbox and framework designed for training and deploying deep learning models to upscale, denoise, and deblur images and videos. It serves as a comprehensive system for image super-resolution and video quality restoration, providing the necessary infrastructure to recover fine visual details and increase pixel density. The project distinguishes itself through specialized toolkits for facial image enhancement and high-fidelity face synthesis, as well as a dedicated video quality restoration suite that utilizes deformable convolutions and generative

This project is a deep learning framework for AI image super-resolution and facial synthesis. It provides a diffusion model image upscaler and a generative facial image synthesizer capable of transforming low-resolution images into high-resolution outputs using pretrained model weights. The system utilizes iterative diffusion refinement and low-resolution guided sampling to restore fine details and sharpness. It supports both unconditional image generation, where images are created from scratch, and guided resolution enhancement for high-fidelity facial reconstruction. The repository include

This project is an AI upscaling framework and deep learning image restorer designed to estimate original source pixels from low-resolution inputs. It functions as a super-resolution reconstruction system that transforms pixelated images into high-resolution versions by restoring high-frequency details and sharpening edges. The system utilizes a convolutional neural network pipeline to analyze pixel data and perform digital image restoration. It employs pixel-shuffle upsampling to rearrange channel dimensions into spatial dimensions, which increases resolution while reducing checkerboard artif

Anime-InPainting is a specialized software platform designed for the restoration of anime illustrations and digital artwork. It functions as a deep learning-based image editor that utilizes generative models to repair damaged or incomplete images, remove unwanted artifacts, and eliminate visual blemishes such as mosaics. The project distinguishes itself through an edge-guided generative approach, which uses structural edge maps to ensure that reconstructed regions maintain visual and spatial consistency with the surrounding image. Users can interact with the restoration process through a grap

This project is a generative adversarial network implementation and research framework. It provides the tools and hyperparameters necessary to train and evaluate generative models across various datasets, specifically designed to reproduce results from academic research. The framework includes a Parzen density likelihood estimator to calculate model log likelihood. This allows for the quantitative evaluation of generative distributions and the measurement of overall model performance. The codebase covers machine learning research capabilities, focusing on the training of adversarial networks

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

PaddleGAN is a generative AI framework and deep learning computer vision library built on the PaddlePaddle framework. It serves as a toolkit for image and video synthesis, providing a collection of generative adversarial network implementations for creating synthetic visual content. The library focuses on advanced synthesis capabilities, including the generation of talking heads through lip motion synchronization and the creation of synthetic videos via motion transfer from driving sequences. It provides tools for domain-to-domain translation, allowing for image style transfer and the transfo

RestorePhotos is an AI face restoration tool and deep learning image upscaler designed to remove blur and reconstruct lost details in degraded facial photographs. It functions as a face photo enhancer and a generative adversarial network image processor that transforms low-quality pixels into high-resolution facial features. The system utilizes a GPU-accelerated inference engine to run machine learning models for real-time image restoration. This hardware acceleration supports the heavy matrix multiplications and tensor-based operations required to sharpen facial images and improve visual fid

This project is a deep learning computer vision implementation focused on low-light image restoration. It uses a neural network to process raw sensor data, mapping underexposed images to well-exposed versions to improve visibility and restore natural colors. The implementation is based on CVPR 2018 research and utilizes TensorFlow to execute the computational graph. It employs a convolutional neural network and pixel-wise regression to reconstruct scene lighting directly from unprocessed raw image data. The project includes a framework for supervised pair learning, where models are trained u

Caffe is a high-performance deep learning framework and convolutional neural network library designed for training and deploying neural networks. It functions as a GPU-accelerated machine learning engine with a core implemented in C++ to enable high-throughput tensor operations. The project utilizes a declarative configuration system where model architectures and hyperparameters are defined in external text files, separating the network design from the execution code. It includes a model serialization system to export trained weights and topologies into binary files for efficient deployment a

Skorch is a library that wraps PyTorch neural networks in a scikit-learn compatible interface, allowing deep learning models to be used within standard machine learning pipelines and hyperparameter optimization tools. It functions as a data adapter, training manager, and optimization tool that bridges the gap between deep learning modules and conventional machine learning workflows. The project distinguishes itself by providing a toolkit for automating the PyTorch training lifecycle, including integrated checkpointing, early stopping, and learning rate scheduling. It further enables transfer

DIGITS is a GPU deep learning training platform and model manager used to train, fine-tune, and manage neural network models on NVIDIA hardware. It functions as a REST-controlled machine learning pipeline that integrates with S3 cloud storage for dataset ingestion and organization. The platform supports image classification workflows, allowing users to train various model architectures and export trained image classifiers for use in external environments. It includes capabilities for model fine-tuning to adapt pretrained weights to specific tasks. The system provides a REST-based API interfa

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

Stable-audio-tools is a toolkit for training and deploying latent diffusion models for high-fidelity audio synthesis. It provides a framework for generating audio by iteratively refining noise within a compressed latent space, using specialized encoders to preserve temporal and spectral features of the audio signal. The project features a system for adapting pre-trained audio checkpoints to new datasets through modular initialization and configuration files. It includes utilities for weight extraction and inference model export, which remove training metadata and optimizer states to create li

This project is a machine learning educational archive and technical documentation collection. It serves as a deep learning tutorial series and implementation guide, providing theoretical explanations and practical walkthroughs for constructing and optimizing neural networks. The content focuses on the design and construction of diverse model architectures, including convolutional neural networks, Long Short-Term Memory networks, and generative adversarial networks. It details specific implementation patterns for autoencoders, sentiment analysis models, and various classification approaches.

This project is a deep learning computer vision library designed for video action recognition. It provides a framework for training and evaluating neural networks that identify and categorize human activities within recorded footage by processing temporal sequences of frames. The library focuses on the implementation of three-dimensional neural network architectures, specifically utilizing three-dimensional convolutional layers to capture both spatial and temporal patterns. By aggregating features across consecutive frame sequences, the models learn to represent the evolution of actions over

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

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 implementation of 3D residual networks designed for video action recognition. It provides a spatiotemporal architecture that analyzes both spatial frames and temporal motion to classify human activities within video clips. The system includes a distributed model training framework to accelerate learning across multiple compute nodes. It supports the deployment and fine-tuning of pre-trained model weights, allowing the adaptation of existing networks to specific new datasets. The codebase covers the full pipeline for spatiotemporal learning, including video dataset p

This project is a deep learning system designed for real-time emotion recognition and facial expression analysis. It utilizes a convolutional neural network architecture to process raw visual input, mapping complex facial patterns to seven distinct emotional states through a supervised machine learning pipeline. The system functions as both a training framework and an inference engine. It includes utilities for preparing and standardizing large image datasets to ensure consistent input quality, alongside a real-time processing pipeline that captures and buffers live video frames to perform co

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 project is a neural style transfer framework that provides a suite of computer vision tools for applying artistic styles to images and video. It functions as a system for training feedforward neural networks, an iterative style optimizer, and a real-time video stylizer. The framework supports two primary methods of stylization: a feedforward model that applies styles in a single pass and an iterative optimization method that generates stylized images by minimizing content and style loss without a pre-trained model. It also enables real-time processing of live webcam feeds using trained m

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

MedSAM is a deep learning framework designed for automating the segmentation of anatomical structures in 2D and 3D medical imagery. It provides specialized tools for fine-tuning pretrained segmentation weights on custom medical datasets and evaluating the accuracy of those predictions against ground truth labels. The project focuses on adapting the Segment Anything Model architecture for medical use, enabling the isolation of specific anatomical structures through prompt-guided methods such as bounding boxes and point prompts. The system covers a full medical AI workflow, including data engi

Pulse is a generative model image upscaler and latent space image processor. It functions as a self-supervised photo upsampling tool that increases image resolution by exploring the latent space of pre-trained generative models to synthesize high-quality details. The system includes a face image alignment tool designed to standardize the scale and orientation of raw facial photos. This preprocessing utility prepares images for higher resolution processing by aligning and downscaling faces to a standard orientation. The project covers AI image super-resolution and generative photo upscaling,

SwinIR is a deep learning image restoration framework that uses Swin Transformer architectures to recover image quality. It is designed to restore degraded images by removing noise, blur, and compression artifacts while increasing pixel density. The model provides specialized capabilities for image super-resolution, image denoising, and image deblurring. It also includes a dedicated tool for the removal of JPEG compression artifacts to restore visual quality lost during encoding. The system focuses on improving overall visual fidelity through resolution upscaling, noise removal, and the reco