1 citations · 1 across the 1 of their papers we have counts for
2 papers
cs.LG2025
DenoMAE2.0: Improving Denoising Masked Autoencoders by Classifying Local Patches
Atik Faysal, Mohammad Rostami, Taha Boushine +3
We introduce DenoMAE2.0, an enhanced denoising masked autoencoder that integrates a local patch classification objective alongside traditional reconstruction loss to improve repres…
cs.LG2025★ 1 cited
DenoMAE: A Multimodal Autoencoder for Denoising Modulation Signals
Atik Faysal, Taha Boushine, Mohammad Rostami +5
We propose Denoising Masked Autoencoder (Deno-MAE), a novel multimodal autoencoder framework for denoising modulation signals during pretraining. DenoMAE extends the concept of mas…