[2111.06377] Masked Autoencoders Are Scalable Vision Learners …
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 …
Masked Autoencoders in Deep Learning - GeeksforGeeks
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 …
PyTorch implementation of MAE https//arxiv.org/abs/2111.06377
title = {Masked Autoencoders Are Scalable Vision Learners}, year = {2021}, The original implementation was in TensorFlow+TPU. This re-implementation is in PyTorch+GPU. This …
Masked Autoencoders Are Effective Tokenizers for Diffusion Models
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.
Masked image modeling with Autoencoders - Keras
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 …
[2505.09160] A Multi-Task Foundation Model for Wireless Channel ...
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 …
How to Implement State-of-the-Art Masked AutoEncoders (MAE)
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 …
Attention-Guided Masked Autoencoders for Learning Image …
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.
Masked Autoencoders: The Hidden Puzzle Pieces of Modern AI
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 …
Spatial-Spectral Hierarchical Multiscale Transformer-Based Masked ...
3 days ago · Spatial-Spectral Hierarchical Multiscale Transformer-Based Masked Autoencoder for Hyperspectral Image Classification ... Transformer, with its powerful long-range relationship …