Nov 13, 2023 · In this article, we introduce an emerging deep learning (DL) architecture — the transformer-masked autoencoder (TMAE) — and discuss its potential in next-generation …

Abstract: This paper shows that masked autoencoders (MAE) are scalable self-supervised learners for computer vision. Our MAE approach is simple: we mask random patches of the …

Oct 11, 2023 · Masked autoencoders (MAE) is a deep learning method based on Transformer. Originally used for images, it has now been extended to video, audio, and some other …

In this article, we explore spectral and spatial remote sensing image features as improved MAE-reconstruction targets. We first conduct a study on reconstructing various image features, all …

3 days ago · In this paper, we introduce Wireless Masked Autoencoder (WiMAE), a pretrained transformer-based wireless channel foundation model based on the MAE framework. WiMAE …

Aug 19, 2024 · Masked autoencoder (MAE), which is based on Transformer architecture, employs a “mask-reconstruction” strategy for training, allowing the model to be effective for downstream …

Our paper presents a novel approach for Single-Label Hyperspectral Image Classifica-tion, demonstrated through the example of a key challenge faced by agricultural seed producers: …

Nov 11, 2021 · This paper shows that masked autoencoders (MAE) are scalable self-supervised learners for computer vision. Our MAE approach is simple: we mask random patches of the …

In this article, we introduce an emerging deep learning (DL) architecture, the transformer-masked autoencoder (TMAE), and discuss its potential in next-generation wireless networks.

Jul 1, 2024 · In this article, we introduce an emerging deep learning (DL) architecture — the transformer-masked autoencoder (TMAE) — and discuss its potential in next-generation …