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Found 6 papers, 1 papers with code
Adversarial training with informed data selection
With the increasing amount of available data and advances in computing capabilities, deep neural networks (DNNs) have been successfully employed to solve challenging tasks in various areas, including healthcare, climate, and finance.
Maximum Likelihood Distillation for Robust Modulation Classification
Deep Neural Networks are being extensively used in communication systems and Automatic Modulation Classification (AMC) in particular.
On the benefits of knowledge distillation for adversarial robustness
To that end, we present Adversarial Knowledge Distillation (AKD), a new framework to improve a model's robust performance, consisting on adversarially training a student on a mixture of the original labels and the teacher outputs.
SafeAMC: Adversarial training for robust modulation recognition models
We propose to use adversarial training, which consists of fine-tuning the model with adversarial perturbations, to increase the robustness of automatic modulation recognition (AMC) models.
On the benefits of robust models in modulation recognition
When analyzing these vulnerable models we found that adversarial perturbations do not shift the symbols towards the nearest classes in constellation space.
Modurec: Recommender Systems with Feature and Time Modulation
Current state of the art algorithms for recommender systems are mainly based on collaborative filtering, which exploits user ratings to discover latent factors in the data.
