CAT: Collaborative Adversarial Training
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Updated
May 31, 2023 - Python
CAT: Collaborative Adversarial Training
Cyber Triad or how to spot Antifragility in Cyber Security.
Gated Domain Units (GDU) aim to make your deep learning models robust against distribution shifts when applied in the real-world.
Main project for the Real-time programming course at NTNU. Coordinating N different elevators in a building, in order to efficiently deal with requests handling the robustness of the system.
Augmentation for CV using frequency shortcuts
AGS: Attribution Guided Sharpening
Implementations accompanying research on Distributional-Shift Robust Reinforcement Learning using Compact Reshaped Observation Processing (CROP)
Official implementation of Don't Look into the Sun: Adversarial Solarization Attacks on Image Classifiers
Official Pytorch implementations of "Generalizable Lightweight Proxy for Robust NAS against Diverse Perturbations" (NeurIPS'23)
Measures stability of the fairness measure for a fair AI
How Fragile is Relation Extraction under Entity Replacements?
Public repository of our assessment work in missing views for EO applications
[ICLR 2023] Official Tensorflow implementation of "Distributionally Robust Post-hoc Classifiers under Prior Shifts"
Learn about the importance of writing reliable and safe code for production environments, particularly in scenarios involving extreme safety measures. We explore NASA's set of rules known as the "Power of 10" that are derived from their experience in developing software for space missions.
Fooling Machine Learning Models: A Novel Out-of-Distribution Attack through Generative Adversarial Networks
Code implementing the experiments described in the NeurIPS 2018 paper "With Friends Like These, Who Needs Adversaries?".
Project for CSE551 Fundamentals of Algorithms written in Clojure.
An experimental setup for my master thesis "Optimizing web extraction queries for robustness"
Code for my Master's Thesis: "The Role of Local Versus Global Features in Convolutional Neural Networks"
Robust Mini-batch Gradient Descent models
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