Official Code for DragGAN (SIGGRAPH 2023)
- artificial-intelligence
- generative-adversarial-network
- generative-models
- image-manipulation
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Official Code for DragGAN (SIGGRAPH 2023)
Image-to-Image Translation in PyTorch
PyTorch implementations of Generative Adversarial Networks.
A list of all named GANs!
Fawkes, privacy preserving tool against facial recognition systems. More info at https://sandlab.cs.uchicago.edu/fawkes
Interactive Image Generation via Generative Adversarial Networks
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
An information security preparedness tool to do adversarial simulation.
Code for "Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks", Gupta et al, CVPR 2018
Generative Adversarial Text to Image Synthesis / Please Star -->
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.
The Simplest DCGAN Implementation
Create adversarial attacks against machine learning Windows malware detectors
The implementation code for "DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction"
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses | 500+ Papers | Perception, Cognition, Planning, Interaction, Agentic System
Synthetic Adversarial Log Objects: A Framework for synthentic log generation
A graph reliability toolbox based on PyTorch and PyTorch Geometric (PyG).
Unsupervised Image to Image Translation with Generative Adversarial Networks
This repository contains the official PyTorch implementation of GeoDA algorithm. GeoDA is a Black-box attack to generate adversarial example for image classifiers.
Claude Code marketplace for multi-agent collaboration — task splitter, output reconciler, adversarial debate, shared memory, acceptance gate. Composes with codex-delegate / gemini-delegate.