Showing 81-100 of 147 projects
Visualizing and understanding the neurons of a GAN with Pytorch-based tools.
State-of-the-art audio codec with 90x compression factor for developers.
Software and pre-trained models for automatic photo quality enhancement using deep learning.
Open source platform for adversary emulation and exposure validation to enhance cybersecurity posture.
A comprehensive collection of data augmentation resources and techniques for developers working with AI tools.
Adversary Tactics - PowerShell Training for security professionals and penetration testers.
A collection of must-read papers on adversarial attacks and defenses for natural language processing.
A simple PyTorch implementation of Generative Adversarial Networks (GANs) for vibe coders exploring AI-powered generative models.
Generates realistic images from text using a Pix2Pix GAN.
A library of code samples and examples for deep learning and computer vision, targeting beginners.
Implementation of various generative adversarial network (GAN) models in popular deep learning frameworks
A PyTorch implementation of Deep Convolutional Generative Adversarial Networks (DCGAN) for developers interested in AI-powered image generation.
The official code repository for the second edition of the book Generative Deep Learning, covering AI models and techniques.
A deep learning library for Haskell, enabling developers to build advanced AI models and applications.
A research framework for easy and efficient training of Generative Adversarial Networks (GANs) based on PyTorch.
A toolbox to generate adversarial examples that fool neural networks in various ML frameworks.
Raptor is an AI-powered security agent that performs adversarial research and attack/defense operations.
WaveGAN is a Python library that enables developers to synthesize raw audio using generative adversarial networks.
A toolbox for adversarial robustness research, focused on building more secure machine learning models.
AttnGAN is a Python library for generating high-quality images from text descriptions using attention-based generative adversarial networks.
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