TRADES (TRadeoff-inspired Adversarial DEfense via Surrogate-loss minimization)

Towards Fairness-Aware Adversarial Learning - CVPR 2024

PyTorch implementation of adversarial attacks [torchattacks]

Practice on cifar100(ResNet, DenseNet, VGG, GoogleNet, InceptionV3, InceptionV4, Inception-ResNetv2, Xception, Resnet In Resnet, ResNext,ShuffleNet, ShuffleNetv2, MobileNet, MobileNetv2, SqueezeNet…

Fast falling sand game made within Godot and utilizing GDExtension features

A base for falling sand cellular automata. You can add liquids, solids, gases.

Final Project for AM 207, Fall 2021. Review & experimentation with paper "Adversarial Examples Are Not Bugs, They Are Features"

Code for ICML paper "Second-Order Provable Defenses against Adversarial Attacks"

Concealed Object Detection, IEEE TPAMI 2021 (CVPR2020 extended version).

Official Pytorch implementation for our AAAI 2023 paper HOTCOLD Block: Fooling Thermal Infrared Detectors with a Novel Wearable Design

Datasets for the paper "Adversarial Examples are not Bugs, They Are Features"

YoloV3 Implemented in Tensorflow 2.0

Convolutional Neural Networks

An adversarial attack on object detectors

Adversarial Texture Optimization from RGB-D Scans (CVPR 2020).

Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples

Adversarial Texture for Fooling Person Detectors in the Physical World

Bitmap & tilemap generation from a single example with the help of ideas from quantum mechanics

An adversarial example library for constructing attacks, building defenses, and benchmarking both

PyTorch implementation of L-GCN [https://arxiv.org/abs/2008.09105]

Code for the paper "Jukebox: A Generative Model for Music"

Python package to easily retrain OpenAI's GPT-2 text-generating model on new texts

Files for our paper, " Musenet : Music Generation Using Abstractive And Generative Methods"

Easily train your own text-generating neural network of any size and complexity on any text dataset with a few lines of code.

Code for the paper "Language Models are Unsupervised Multitask Learners"