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Found 5 papers, 1 papers with code
Bayes Conditional Distribution Estimation for Knowledge Distillation Based on Conditional Mutual Information
To improve this estimate for KD, in this paper we introduce the concept of conditional mutual information (CMI) into the estimation of BCPD and propose a novel estimator called the maximum CMI (MCMI) method.
Robustness Against Adversarial Attacks via Learning Confined Adversarial Polytopes
Deep neural networks (DNNs) could be deceived by generating human-imperceptible perturbations of clean samples.
AdaFed: Fair Federated Learning via Adaptive Common Descent Direction
AdaFed adaptively tunes this common direction based on the values of local gradients and loss functions.
Conditional Mutual Information Constrained Deep Learning for Classification
The concepts of conditional mutual information (CMI) and normalized conditional mutual information (NCMI) are introduced to measure the concentration and separation performance of a classification deep neural network (DNN) in the output probability distribution space of the DNN, where CMI and the ratio between CMI and NCMI represent the intra-class concentration and inter-class separation of the DNN, respectively.
Thundernna: a white box adversarial attack
At the same time, the attack against a neural network is the key to improving its robustness.
