12 Weeks, 24 Lessons, AI for All!
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Updated
May 29, 2024 - Jupyter Notebook
Generative adversarial networks (GAN) are a class of generative machine learning frameworks. A GAN consists of two competing neural networks, often termed the Discriminator network and the Generator network. GANs have been shown to be powerful generative models and are able to successfully generate new data given a large enough training dataset.
12 Weeks, 24 Lessons, AI for All!
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!❤
HyMPS will be a platform-indipendent software suite for advanced audio/video contents production.
List of protein (enzymes and PPIs) conformations and molecular dynamics using generative artificial intelligence and deep learning
The collection of pre-trained, state-of-the-art AI models for ailia SDK
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.
We well know GANs for success in the realistic image generation. However, they can be applied in tabular data generation. We will review and examine some recent papers about tabular GANs in action.
Synthetic data generation for tabular data
[CVPR 2023, top-10%] Authors official PyTorch implementation of the "Attribute-preserving Face Dataset Anonymization via Latent Code Optimization".
Using WGAN-gp and creating art portraits.
Generate Art
so-vits-svc fork with realtime support, improved interface and more features.
Repo for all the SRIP 2024 work at CVIG Lab IITGN under Prof. Shanmuganathan Raman
This repository leverages Generative Adversarial Networks (GANs) to enhance image resolution for various applications, using the Super-Resolution GAN (SRGAN) architecture. The project includes a Jupyter Notebook for model training and a detailed research paper documenting the methodology and results.
A Great Collection of Deep Learning Tutorials and Repositories
Synthetic data generators for tabular and time-series data
A library to generate synthetic tabular or RDF data using Conditional Generative Adversary Networks (GANs) combined with Differential Privacy techniques.
《李宏毅深度学习教程》(李宏毅老师推荐👍),PDF下载地址:https://github.com/datawhalechina/leedl-tutorial/releases
Released June 10, 2014