**RandomResizedCrop** is a type of image data augmentation where a crop of random size of the original size and a random aspect ratio of the original aspect ratio is made. This crop is finally resized to given size.

Image Credit: [Apache MXNet](https://mxnet.apache.org/versions/1.5.0/tutorials/gluon/data_augmentation.html)

**AEDA**, or **An Easier Data Augmentation**, is a type of data augmentation technique for text classification which includes only the insertion of various punctuation marks into the input sequence. AEDA preserves all the input information and does not mislead the network since it keeps the word order intact while changing their positions in that the words are shifted to the right.

AEDA

AEDA: An Easier Data Augmentation Technique for Text Classification

Random Resized Crop

A **Sparse Autoencoder** is a type of autoencoder that employs sparsity to achieve an information bottleneck. Specifically the loss function is constructed so that activations are penalized within a layer. The sparsity constraint can be imposed with [L1 regularization](https://paperswithcode.com/method/l1-regularization) or a KL divergence between expected average neuron activation to an ideal distribution $p$.

Image: [Jeff Jordan](https://www.jeremyjordan.me/autoencoders/). Read his blog post (click) for a detailed summary of autoencoders.

Year	2000
Data Source	Papers with Code archive (CC BY-SA)