acgan

毕业设计:基于深度学习的土地覆盖分类方法研究

All GAN models in Keras

Hybrid WCGAN-ACGAN framework for balanced network intrusion detection on NSL-KDD and UNSW-NB15 datasets using XGBoost, Decision Trees, CNN, and AutoGluon classifiers

PyTorch conditional WGAN-GP generating Office-Home domain feature vectors, with t-SNE plots comparing real vs generated features

Research for text-to-image synthesis via modified auxiliary classifier GANs. Incremental modification of model architecture for improved results, fully documented.

Deep learning–based CKD classification using CT kidney images. Includes SMOTE and ACGAN data augmentation, CNN models (VGG16, EfficientNetV2, MobileNetV2), source code, and trained weights for reproducible medical image analysis.

Memory Replay GANs: learning to generate images from new categories without forgetting

This software can be used to convert a sequence of digits to a video where the hand-written representations of those digits are twining from one to the other.

This repository provides tools to train and evaluate the Genome-AC-GAN model for generating realistic artificial human genomes.

coverless_information_hiding with acgan

This is the repository of Deep Learning for Computer Vision at National Taiwan University.

Personal repository to learn about different types of GAN models.

Classic Augmentation Based Classifier Generative Adversarial Network (CACGAN) for COVID-19 Diagnosis

Training Generative Adversarial Network models for scRNA-seq datasets

A Simple code to train a CNN to predict label of Covid and Non-Covid CT scan images and an ACGAN to generate them.

An Auxiliary Classifier GAN (ACGAN) in pytorch to generate MNIST digits.

some models with easy understand code

Generative Adversarial Network Implementations in Keras

Playing with MNIST. Machine Learning. Generative Models.

ProGAN with Standard, WGAN, WGAN-GP, LSGAN, BEGAN, DRAGAN, Conditional GAN, InfoGAN, and Auxiliary Classifier GAN training methods