robustness-experiments

There are 0 repository under robustness-experiments topic.

cemanil / LNets
Lipschitz Neural Networks described in "Sorting Out Lipschitz Function Approximation" (ICML 2019).
robustness-experiments gan-training lipschitz-functions universal-approximation maxmin expressiveness wasserstein-distance-estimation adversarial-robustness groupsort
Language:Python 52
shashankkotyan / RobustArchitectureSearch
This github repository contains the official code for the paper, "Evolving Robust Neural Architectures to Defend from Adversarial Attacks"
neuroevolution deep-learning deep-neural-networks evolutionary-algorithms evolutionary-strategy evolution-strategies evolutionary-algorithm evolutionary-computation architecture-search neural-network neural-networks neural-architecture-search neuralnetwork neuralnetworks neural-nets adversarial-machine-learning robustness robustness-experiments robustness-verification paper
Language:Python 18
sayakpaul / Consistency-Training-with-Supervision
Contains experimentation notebooks for my Keras Example "Consistency Training with Supervision".
tensorflow keras robustness-experiments consistency-regularization computer-vision
Language:Jupyter Notebook 4
XavierSpycy / NumPyNMF
NumPyNMF implements nine different Non-negative Matrix Factorization (NMF) algorithms using NumPy library and compares the robustness of each algorithm to five various types of noise in real-world data applications.
non-negative-matrix-factorization numpy-library robustness-experiments
Language:Python 2
shashankkotyan / DualQualityAssessment
This github repository contains the official code for the papers, "Robustness Assessment for Adversarial Machine Learning: Problems, Solutions and a Survey of Current Neural Networks and Defenses" and "One Pixel Attack for Fooling Deep Neural Networks"
paper adversarial-machine-learning adversarial-search neural-network robustness robustness-experiments robustness-verification deep-learning deep-neural-networks differential-evolution cmaes pixel-attack threshold-attack dual-quality-assessment robustness-assessment adversarial-attack adversarial-defence evolutionary-algorithm danilo-vasconcellos-vargas shashank-kotyan
Language:Python 1
luismarcoslc / understanding_deep_belief_networks
Comparison of DBNs and FFNN, stressing on understanding how DBNs work and how robust they are against noise and adversarial attacks.
deep-belief-network feedforward-neural-network robustness-experiments tensorflow
Language:Jupyter Notebook