davidstutz

davidstutz's Stars

• aleju/imgaug

  Image augmentation for machine learning experiments.

  Language:Python14.4k2295172.4k

• kuangliu/pytorch-cifar

  95.47% on CIFAR10 with PyTorch

  Language:Python6k851362.1k

• DeepVoltaire/AutoAugment

  Unofficial implementation of the ImageNet, CIFAR 10 and SVHN Augmentation Policies learned by AutoAugment using pillow

  Language:Jupyter Notebook1.5k2232199

• DSE-MSU/DeepRobust

  A pytorch adversarial library for attack and defense methods on images and graphs

  Language:Python99417107192

• sylvainhalle/textidote

  Spelling, grammar and style checking on LaTeX documents

  Language:Java942720469

• MadryLab/robustness

  A library for experimenting with, training and evaluating neural networks, with a focus on adversarial robustness.

  Language:Jupyter Notebook9171983181

• wohlert/semi-supervised-pytorch

  Implementations of various VAE-based semi-supervised and generative models in PyTorch

  Language:Python707130123

• RobustBench/robustbench

  RobustBench: a standardized adversarial robustness benchmark [NeurIPS 2021 Benchmarks and Datasets Track]

  Language:Python670911199

• fra31/auto-attack

  Code relative to "Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks"

  Language:Python659969112

• ptrblck/pytorch_misc

  Code snippets created for the PyTorch discussion board

  Language:Python546171164

• uoguelph-mlrg/Cutout

  2.56%, 15.20%, 1.30% on CIFAR10, CIFAR100, and SVHN https://arxiv.org/abs/1708.04552

  Language:Python5441219156

• yaodongyu/TRADES

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

  Language:Python5221028124

• tolstikhin/wae

  Wasserstein Auto-Encoders

  Language:Python508191391

• bmsookim/wide-resnet.pytorch

  Best CIFAR-10, CIFAR-100 results with wide-residual networks using PyTorch

  Language:Python4611416129

• locuslab/smoothing

  Provable adversarial robustness at ImageNet scale

  Language:Python36912974

• ericjang/normalizing-flows-tutorial

  Tutorial on normalizing flows.

  Language:Jupyter Notebook29212465

• mahyarnajibi/FreeAdversarialTraining

  PyTorch Implementation of Adversarial Training for Free!

  Language:Python23971042

• ex4sperans/variational-inference-with-normalizing-flows

  Reimplementation of Variational Inference with Normalizing Flows (https://arxiv.org/abs/1505.05770)

  Language:Python2287442

• Hadisalman/smoothing-adversarial

  Code for our NeurIPS 2019 *spotlight* "Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers"

  Language:Python22391139

• cg563/simple-blackbox-attack

  Code for ICML 2019 paper "Simple Black-box Adversarial Attacks"

  Language:Python19562656

• coallaoh/Principles

  178804

• csdongxian/AWP

  Codes for NeurIPS 2020 paper "Adversarial Weight Perturbation Helps Robust Generalization"

  Language:Python1703419

• prateekmehta59/Celebrity-Face-Recognition-Dataset

  Dataset of around 800k images consisting of 1100 Famous Celebrities and an Unknown class to classify unknown faces

  Language:Python1422639

• meder411/PyTorch-EMDLoss

  PyTorch 1.0 implementation of the approximate Earth Mover's Distance

  Language:Cuda13651013

• elliothe/BFA

  Pytorch implementation of Bit-Flip based adversarial weight Attack (BFA)

  Language:Jupyter Notebook302510

• jakc4103/piecewise-quantization

  PyTorch implementation of Near-Lossless Post-Training Quantization of Deep Neural Networks via a Piecewise Linear Approximation

  Language:Python21503

• ritheshkumar95/pytorch-normalizing-flows

  Language:Python17000

• hysts/pytorch_cutout

  A PyTorch implementation of Cutout

  Language:Python15111

• astyonax/CFFI-numpy-openMP

  CFFI + Numpy + OpenMP -- a reference example

  Language:Python5401

• GavinAbercrombie/medical-safety

  Data from the paper "Risk-graded Safety for Handling Medical Queries in Conversational AI"

  Language:Jupyter Notebook2101