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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 repository provides structured code examples and project templates designed for classroom instruction in machine learning and neural networks. It offers reference implementations of deep learning models for both computer vision and natural language processing tasks, built using PyTorch as the core framework. The codebase is organized as a modular project template with separate directories for data handling, model definitions, and training scripts, promoting reusability and clarity. It includes predefined pipelines for image classification and text processing, along with a command-line in
A PyTorch implementation of "DGC-Net: Dense Geometric Correspondence Network"
Base pretrained models and datasets in pytorch (MNIST, SVHN, CIFAR10, CIFAR100, STL10, AlexNet, VGG16, VGG19, ResNet, Inception, SqueezeNet)
:couple: Joint Discriminative and Generative Learning for Person Re-identification. CVPR'19 (Oral) :couple:
The main features of nvlabs/dg-net are: Computer Vision Models, Generative Adversarial Networks, Learning and Automation, Infrastructure and Tools.
Projects with overlapping indexed features include: kozistr/awesome-gans — Awesome-GANs is a curated resource list and research repository focused on the development and evaluation of… cs230-stanford/cs230-code-examples — This repository provides structured code examples and project templates designed for classroom instruction in machine… aaltovision/dgc-net — A PyTorch implementation of "DGC-Net: Dense Geometric Correspondence Network". adamian98/pulse — Pulse is a generative model image upscaler and latent space image processor. It functions as a self-supervised photo… alaphao/coremlexample — An example of CoreML using a pre-trained VGG16 model. aaron-xichen/pytorch-playground — Base pretrained models and datasets in pytorch (MNIST, SVHN, CIFAR10, CIFAR100, STL10, AlexNet, VGG16, VGG19, ResNet,…