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adversarial-robustness-toolbox

Python library for adversarial machine learning (evasion, extraction, poisoning, verification, certification) with attacks and defences for neural networks, logistic regression, decision trees, SVM, gradient boosted trees, Gaussian processes and more with multiple framework support

apt

One Prompt Word is Enough to Boost Adversarial Robustness for Pre-trained Vision-Language Models

cifar10-r

CIAFR10-R(endition): a downsampled variant of ImageNet-R(endition)

cntk-hotel-pictures-classificator

This project is a part of my MSc degree in Department of Computing, Imperial College London. This project is done under the efficient collaboration with my classmates Tomasz Bartkowiak, Danlin Peng, Yini Fang, Suampa Ketpreechasawat, and Nattapat Chaimanowong. We appreciate much all sincere and helpful advices received from co-supervisors, Anandha Gopalan (Imperial College London), Lee Stott (Microsoft), Tempest van Schaik (Microsoft) and Geoff Hughes (Microsoft), and the sponsor from Microsoft regarding Azure.

cntk_room-recognition_datasets

combating-ro-advlc

Combating robust overfitting in adversarial training via AdvLC

da-alone-improves-at

data augmentation alone can improve adversarial training

facial-expression-recognition_decision-tree

Facial Expression Classificaion using Decision Trees with input of AUs

facial-expression-recognition_neural-network

gaussian-process

inner-detectors

An inner detector is a unit, representing a semantic concept in the raw input image, in an intermediate layer. This project is designed to identify inner detectors, explore their properties and transfer them. The goal of project is to build an high interpretable network via transferring inner detectors.

instance-adaptive-smoothness-enhanced-at

Instance adaptive Smoothness Enhanced Adversarial Training (ISEAT)

interpreting-techniques

the implementation of selected popular techniques for interpreting the complicated machine learning models esp. Deep Neural Networks.

oodrobustbench

The code base of OODRobustBench

rnn-from-scratch

This is a tutorial for personal practice regarding building RNNs from scratch.

sampling_mcmc_gibbs

treelli.github.io

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