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Bethge Lab's Projects

cifar10_challenge icon cifar10_challenge

A challenge to explore adversarial robustness of neural networks on CIFAR10.

convex_adversarial icon convex_adversarial

A method for training neural networks that are provably robust to adversarial attacks.

datatypeidentification icon datatypeidentification

Code for the ICLR'24 paper: "Visual Data-Type Understanding does not emerge from Scaling Vision-Language Models"

decompose icon decompose

Blind source separation based on the probabilistic tensor factorisation framework

deeplabcut icon deeplabcut

Markerless tracking of user-defined features with deep learning

docker icon docker

Information and scripts to run and develop the Bethge Lab Docker containers

foolbox icon foolbox

A Python toolbox to create adversarial examples that fool neural networks in PyTorch, TensorFlow, and JAX

game-of-noise icon game-of-noise

Trained model weights, training and evaluation code from the paper "A simple way to make neural networks robust against diverse image corruptions"

lifelong-benchmarks icon lifelong-benchmarks

Benchmarks introduced in the paper: "Lifelong Benchmarks: Efficient Model Evaluation in an Era of Rapid Progress"

magapi-wrapper icon magapi-wrapper

Wrapper around Microsoft Academic Knowledge API to retrieve MAG data

mesos icon mesos

Development of Mesos cluster

mmdetection icon mmdetection

Fork of the MMDetection Toolbox containing the Robustness Benchmark from the paper "Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming" (merged)

mnist_challenge icon mnist_challenge

A challenge to explore adversarial robustness of neural networks on MNIST.

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