一个用于肺炎图像分类的轻量级ResNet18-SAM模型实现，采用SH-DCGAN生成少类样本数据，解决了数据不平衡的问题，同时结合剪枝策略实现轻量化！MedGAN-ResLite-V2 Released! Stay tuned!❤

This repository contains an implementation of a Deep Convolutional Generative Adversarial Network (DCGAN) trained on the FashionMNIST dataset. The project aims to generate realistic images of clothing items using a GAN architecture. It includes model definitions, training scripts, and visualizations of generated images at various training stages.

DeepNude's algorithm and general image generation theory and practice research, including pix2pix, CycleGAN, UGATIT, DCGAN, SinGAN, ALAE, mGANprior, StarGAN-v2 and VAE models (TensorFlow2 implementation). DeepNude的算法以及通用生成对抗网络（GAN,Generative Adversarial Network）图像生成的理论与实践研究。

Implement a GAN for Celeb_a dataset to generate celebrities faces with expert mode of TensorFlow

Yapay Zeka dersi proje ödevi. Google Colab üzerinde DCGAN (Deep Convolutional Generative Adversarial Network) kullanarak yağlı boya insan portreleri üreten bir yapay zeka modelini içermektedir.Projenin amacı, yapay zeka ile ilgili temel bilgiler edinmektir.

Implement a GAN for Fashion Mnist dataset to generate digits

🖼️ A handwritten number generator build with PyTorch.

Artificial Intelligence

We implement a conditional Deep Convolutional Generative Adversarial Network (DCGAN) sampling high-quality Street View House Numbers (SVHN), conditioned on an embedding of a desired label.

A PhotoReaslistic AI GAN model to generate photorealistic faces on a large scale

A template repository for GANs

Generating Realistic Human Faces with Generative Networks

Generative Adversarial Network for Nintendo Entertainment System Music.

Tasks for Architecture of Neural Networks Course @ ITMO University

Deep Learning Super Sampling with Deep Convolutional Generative Adversarial Networks.

Implementation of basic DCGAN (deep convolutional generative adversarial network), inspired heavily from Jason Brownlee's post on machinelearningmastery.com.

In This Repo I've Built DC-GAN as a part of Gan Series I'm Building

A Conditional Deep Convolutional Generative Adversarial Network implemented in PyTorch, trained on the Fashion MNIST dataset.

Code for our paper "Generative Adversarial Network with Soft-Dynamic Time Warping and Parallel Reconstruction for Energy Time Series Anomaly Detection" and its extension.