Algorithms for outlier, adversarial and drift detection
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Updated
Jul 1, 2024 - Python
Algorithms for outlier, adversarial and drift detection
Sentiment Analysis, Text Classification, Text Augmentation, Text Adversarial defense, etc.;
Pytorch implementation of projected gradient descent (PGD) adversarial noise attack
This study provides a comprehensive comparison of the different algorithms implemented on a reservoir system, and the results are statistically analyzed from the results of other machine learning algorithms. It generates new data which is passed on from the discriminator of the Generative Adversarial Network.
Official TensorFlow Implementation of Adversarial Training for Free! which trains robust models at no extra cost compared to natural training.
La nostra soluzione per la Tablut Challenge 2022 ♟️ (Fondamenti di Intelligenza Artificiale M)
💡 Adversarial attacks on explanations and how to defend them
The official repository for CosPGD: a unified white-box adversarial attack for pixel-wise prediction tasks.
Adversarial Training for Natural Language Understanding
Tool, paper, and study data for DeepManeuver: Adversarial Test Generation for Trajectory Manipulation of Autonomous Vehicles.
[Nature Machine Intelligence Journal] Official pytorch implementation for Uncertainty-Guided Dual-Views for Semi-Supervised Volumetric Medical Image Segmentation
Combination of Distributed Adversarial Training and JointSpar-Lars to experiment the effects of sparsifying gradients and their computation on Distributed Adversarial Training.
Official code for the paper "Adversarial Magnification to Deceive Deepfake Detection through Super Resolution"
Source code for COLING 2020 paper "Enhancing Neural Models with Asymmetrical Vulnerability via Adversarial Attack"
Official Implementation of Progressively Volumetrized Deep Generative Models for Data-Efficient Contextual Learning of MR Image Recovery
Battleship environment for reinforcement learning tasks
[CIKM 2022] Self-supervision Meets Adversarial Perturbation: A Novel Framework for Anomaly Detection (PyTorch)
Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation
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