IAM

DAVIDSTUTZ

TAG»PYTHON«

18thAUGUST2022

PROJECT

Examples, tools and resources for using Caffe’s Python interface pyCaffe.

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17thAUGUST2022

PROJECT

A template for extending PyTorch using C/CUDA operations.

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ARTICLE

Code Released: Conformal Training

The code for our ICLR’22 paper on learning optimal conformal classifiers is now available on GitHub. The repository not only includes our implementation of conformal training but also relevant baselines such as coverage training and several conformal predictors for evaluation. Furthermore, it allows to reproduce the majority of experiments from the paper.

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16thAUGUST2022

PROJECT

Python implementation of probabilistic principal component analysis (PPCA).

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16thAUGUST2022

PROJECT

3D mesh fusion, voxelization and evaluation for computer vision research.

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ARTICLE

Code Released: Adversarial Robust Generalization and Flatness

The code for my ICCV’21 paper relating adversarial robustness to flatness in the (robust) loss landscape is now available on GitHub. The repository includes implementations of various adversarial attacks, adversarial training variants and “attacks” on model weights in order to measure robust flatness.

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ARTICLE

Code Released: Adversarial Patch Training

The code for our paper on adversarial patch training on location-optimized adversarial patches is now available on GitHub. The repository includes a PyTorch implementation of our adversarial patch attack with location optimization as well as an adversarial training routine. The experiments on Cifar10 and GTSRB presented in the paper can easily be reproduced.

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06thMAY2020

PROJECT

Adversarial training on location-optimized adversarial patches.

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ARTICLE

Code Released: Confidence-Calibrated Adversarial Training

The code for my latest paper on confidence-calibrated adversarial training has been released on GitHub. The repository does not only include a PyTorch implementation of confidence-calibrated adversarial training, but also several white- and black box attacks to generate adversarial examples and the proposed confidence-thresholded robust test error. Furthermore, these implementations are fully tested and allow to reproduce the results from the paper. This article gives an overview of the repository and highlights its features and components.

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ARTICLE

FONTS: A Synthetic MNIST-Like Dataset with Known Manifold

In deep learning and computer vision, data is often assumed to lie on a low-dimensional manifold, embedded within the potentially high-dimensional input space — as, for example, for images. However, the manifold is usually not known which hinders deeper understanding of many phenomena in deep learning, such as adversarial examples. Based on my recent CVPR’19 paper, I want to present FONTS, a MNIST-like, synthetically created dataset with known manifold to study adversarial example.

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