We can also see that the places for the patches are different every time which shows the type of masking on the image. That can be centred masking, block-wise masking or random masking. In the above ...

This can be considered as an implementation of a masked autoencoder for self-supervised pre-training with the CIFAR-10 data. Applications of Masked Image Modelling. In the above sections, we have seen ...

The repository gives an improved Transformer-ConvNet architecture with Masked AutoEncoder for Cardiac MRI Registration. The core of the Transformer is designed as a Masked AutoEncoder (MAE) and a ...

Unlike other deep learning (DL) models, Transformer has the ability to extract long-range dependency features from hyperspectral image (HSI) data. Masked autoencoder (MAE), which is based on ...

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 ...

Recently masked autoencoder (MAE) has achieved great success in visual representation learning and delivered promising potential in many downstream vision tasks. However, due to the lack of a saliency ...

Keywords: neural segmentation, SEM image, masked autoencoder, image segmentation, self-supervised learning. Citation: Cheng A, Shi J, Wang L and Zhang R (2023) Learning the heterogeneous ...