This repository focuses on a masked autoencoder based on a Convolutional Neural Network (CNN). Initially, it is used for self-supervised learning to extract features from the MNIST dataset by ...

[ICLR'23 Spotlight🔥] The first successful BERT/MAE-style pretraining on any convolutional network; Pytorch impl. of "Designing BERT for Convolutional Networks: Sparse and Hierarchical Masked Modeling ...

To tackle this problem, a unified HSI masked autoencoder framework was proposed for HSI classification. Different from existing works, the hyperspectral image masked autoencoder (HSIMAE) framework was ...

The representation ability of the model is strongly correlated with the number of such high-quality labels. Recently, the masked autoencoder (MAE) has been shown to effectively pre-train Vision ...

Therefore, we devise a scalable self-supervised network called instructional mask autoencoder, which can extract general patterns of HSIs by these unannotated data. It primarily consists of a ...