Modules used in [GAN](https://paperswithcode.com/method/gan)'s style transfer.

Random Erasing is a data augmentation method for training the convolutional neural network (CNN), which randomly selects a rectangle region in an image and erases its pixels with random values. In this process, training images with various levels of occlusion are generated, which reduces the risk of over-fitting and makes the model robust to occlusion. Random Erasing is parameter learning free, easy to implement, and can be integrated with most of the CNN-based recognition models. Random Erasing is complementary to commonly used data augmentation techniques such as random cropping and flipping, and can be implemented in various vision tasks, such as image classification, object detection, semantic segmentation.

Random Erasing

Random Erasing Data Augmentation

Style Transfer Module

Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization

The motivation of this work is based on the hypothesis that historical values of the acquisition function are good predictors of their future values. This idea is quite intuitive. For example, once a model is certain about its predictions on a given sample, this fact is unlikely to change. This can be explained by the randomness in the training, especially when using small acquisition sizes.

Source	Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization
Year	2000
Data Source	Papers with Code archive (CC BY-SA)