View source for 17875210. Masked Autoencoders for Computer Vision simplified abstract (META PLATFORMS, INC.)

=Masked Autoencoders for Computer Vision=


==Organization Name==

[[:Category:META PLATFORMS, INC.|META PLATFORMS, INC.]]


[[Category:META PLATFORMS, INC.]]

==Inventor(s)==

[[:Category:Kaiming He of Palo Alto CA (US)|Kaiming He of Palo Alto CA (US)]][[Category:Kaiming He of Palo Alto CA (US)]]

[[:Category:Piotr Dollar of San Mateo CA (US)|Piotr Dollar of San Mateo CA (US)]][[Category:Piotr Dollar of San Mateo CA (US)]]

[[:Category:Ross Girshick of Seattle WA (US)|Ross Girshick of Seattle WA (US)]][[Category:Ross Girshick of Seattle WA (US)]]

[[:Category:Saining Xie of Sunnyvale CA (US)|Saining Xie of Sunnyvale CA (US)]][[Category:Saining Xie of Sunnyvale CA (US)]]

[[:Category:Xinlei Chen of Belmont CA (US)|Xinlei Chen of Belmont CA (US)]][[Category:Xinlei Chen of Belmont CA (US)]]

[[:Category:Yanghao Li of Sunnyvale CA (US)|Yanghao Li of Sunnyvale CA (US)]][[Category:Yanghao Li of Sunnyvale CA (US)]]

==Masked Autoencoders for Computer Vision - A simplified explanation of the abstract==


This abstract first appeared for US patent application 17875210 titled 'Masked Autoencoders for Computer Vision

==Simplified Explanation==

The abstract describes a patent application for a computing system that pre-trains a machine-learning model using a plurality of images. The system divides each image into patches, processes visible patches with an encoder, and reconstructs masked patches with a decoder to update the model based on comparisons between the original and reconstructed images.

* The system pre-trains a machine-learning model using a set of images.
* Images are divided into patches, with some patches visible and others masked during pre-training.
* Visible patches are processed by an encoder to generate latent representations.
* Masked patches are reconstructed by a decoder using latent representations and mask tokens.
* The model is updated based on comparisons between original and reconstructed images.

== Potential Applications ==
This technology could be applied in image recognition, computer vision, and data compression systems.

== Problems Solved ==
This technology helps improve the accuracy and efficiency of machine-learning models by pre-training them with a diverse set of images.

== Benefits ==
The benefits of this technology include enhanced model performance, better image reconstruction, and improved training efficiency.

== Potential Commercial Applications ==
A potential commercial application for this technology could be in developing advanced image processing software for industries such as healthcare, security, and entertainment.

== Possible Prior Art ==
Prior art in this field may include research papers or patents related to image processing, machine learning, and neural networks.

=== Unanswered Questions ===

=== How does this technology compare to existing image pre-training methods? ===
This technology offers a unique approach to pre-training machine-learning models using image patches and a combination of visible and masked patches. It would be interesting to see a comparison with traditional pre-training methods to evaluate its effectiveness and efficiency.

=== What impact could this technology have on the development of AI systems in various industries? ===
Understanding the potential implications of implementing this technology in different sectors could provide insights into its scalability, adaptability, and overall impact on the advancement of AI systems.


==Original Abstract Submitted==

In particular embodiments, a computing system may access a plurality of images for pre-training a first machine-learning model that includes an encoder and a decoder. Using each image, the system may pre-train the model by dividing the image into a set a patches, selecting a first subset of the patches to be visible and a second subset of the patches to be masked during the pre-training, processing, using the encoder, the first subset of patches to generate corresponding first latent representations, processing, using the decoder, the first latent representations corresponding to the first subset of patches and mask tokens corresponding to the second subset of patches to generate reconstructed patches corresponding to the second subset of patches, the reconstructed patches and the first subset of patches being used to generate a reconstructed image, and updating the model based on comparisons between the image and the reconstructed image.



[[Category:G06V10/778]]
[[Category:G06V10/26]]
[[Category:G06V10/75]]
[[Category:G06V10/774]]