前言

接下來出不介紹影像增量的問題。

影像增量

As for our tasks there is very little training data available, we use excessive data augmentation by applying elastic deformations to the available training images.

使用彈性變形增量影像。

This allows the network to learn invariance to such deformations, without the need to see these transformations in the annotated image corpus.

這可以讓增加樣本的多樣性

This is particularly important in biomedical segmentation, since deformation used to be the most common variation in tissue and realistic deformations can be simulated efficiently.

這種變形是在生物資訊中很多。

The value of data augmentation for learning invariance has been shown in Dosovitskiy et al. [2] in the scope of unsupervised feature learning.

[2] 文章中證實了數據增強對訓練有不變性。

結語

這邊說明了彈性增量的優點，實作的時候來看看到底這種是什麼增量吧。

參考文獻

[0] U-net