Nov 11, 2021 · 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 input image and reconstruct the missing pixels. It is based on two core designs.

Jul 8, 2024 · Masked autoencoders are neural network models designed to reconstruct input data from partially masked or corrupted versions, helping the model learn robust feature representations. They are significant in deep learning for tasks such as data denoising, anomaly detection, and improving model generalization by training on incomplete data.

title = {Masked Autoencoders Are Scalable Vision Learners}, year = {2021}, The original implementation was in TensorFlow+TPU. This re-implementation is in PyTorch+GPU. This repo is a modification on the DeiT repo. Installation and preparation follow that repo.

Feb 5, 2025 · Motivated by these insights, we propose MAETok, an autoencoder (AE) leveraging mask modeling to learn semantically rich latent space while maintaining reconstruction fidelity.

Dec 20, 2021 · Inspired from the pretraining algorithm of BERT (Devlin et al.), they mask patches of an image and, through an autoencoder predict the masked patches. In the spirit of "masked language modeling", this pretraining task could be referred to as "masked image modeling".

3 days ago · Current applications of self-supervised learning to wireless channel representation often borrow paradigms developed for text and image processing, without fully addressing the unique characteristics and constraints of wireless communications. Aiming to fill this gap, we first propose WiMAE (Wireless Masked Autoencoder), a transformer-based encoder-decoder foundation model pretrained on a ...

Sep 16, 2024 · Today, I’m excited to delve into one of the most significant breakthroughs in Computer Vision post-Vision Transformers: Masked Autoencoders (MAE). This article serves as the practical implementation companion to my previous post: The Ultimate Guide to Masked Autoencoders (MAE)

TL;DR: We guide the reconstruction learning of a masked autoencoder with attention maps to learn image represenations with an improved high-level semantic understanding.

Nov 21, 2024 · At its heart, a Masked Autoencoder is a self-supervised learning model designed to understand data by reconstructing its masked components. Initially inspired by the success of masked language models like BERT, the concept has since been adapted and optimized for computer vision and other domains.

3 days ago · Spatial-Spectral Hierarchical Multiscale Transformer-Based Masked Autoencoder for Hyperspectral Image Classification ... Transformer, with its powerful long-range relationship modeling ability, has become a popular model; however, it usually requires a large number of labeled data for parameter training, which may be costly and impractical for ...