DenoMAE adds noise as a fifth modality to a masked autoencoder and claims data-efficient denoising and modulation classification, but its efficiency claim is not yet supported by matched experiments.
Exploring the limits of transfer learning with a unified text-to-text transformer,
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DenoMAE: A Multimodal Autoencoder for Denoising Modulation Signals
DenoMAE adds noise as a fifth modality to a masked autoencoder and claims data-efficient denoising and modulation classification, but its efficiency claim is not yet supported by matched experiments.