# CSE5519 Advances in Computer Vision (Topic F: 2022: Representation Learning)

## Masked Autoencoders Are Scalable Vision Learners

[link to the paper](https://openaccess.thecvf.com/content/CVPR2022/papers/He_Masked_Autoencoders_Are_Scalable_Vision_Learners_CVPR_2022_paper.pdf)

### Novelty in MAE

#### Masked Autoencoders

Masked Autoencoders are a type of autoencoders that mask out some of the input data and train the model to reconstruct the original data. For best performance, they mask out 75% of the input data.

A masked auto encoder with a single-block decoder can perform strongly with fine tuning.

This method speeds up the training process by a factor of 3-4

> [!TIP]
>
> This paper shows a new way to train a vision model by using masked autoencoders. The authors masked out 75% of the input data and train the model to reconstruct the original data by the insight that image data is highly redundant compared to text data when using transformer architecture.
>
> Currently, the sampling method is uniform and simple with surprising results. I wonder if we could use better sampling method, for example uniform sampling with information entropy on each patches would yield better results?