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Found 6 papers, 5 papers with code
Membership Inference Attack Using Self Influence Functions
Member inference (MI) attacks aim to determine if a specific data sample was used to train a machine learning model.
Generative Adversarial Networks
Generative Adversarial Networks (GANs) are very popular frameworks for generating high-quality data, and are immensely used in both the academia and industry in many domains.
Simple Post-Training Robustness Using Test Time Augmentations and Random Forest
A leading defense against such attacks is adversarial training, a technique in which a DNN is trained to be robust to adversarial attacks by introducing adversarial noise to its input.
Detecting Adversarial Samples Using Influence Functions and Nearest Neighbors
We use influence functions to measure the impact of every training sample on the validation set data.
Road Scene Understanding by Occupancy Grid Learning from Sparse Radar Clusters using Semantic Segmentation
Occupancy grid mapping is an important component in road scene understanding for autonomous driving.
DNN or k-NN: That is the Generalize vs. Memorize Question
Moreover, the behavior of DNNs compared to the linear classifiers SVM and LR is quite the same on the training and test data regardless of whether the network generalizes.
