[day-28] U-net Conclusion

前言

要道尾声了，Unet的结论对於这们短的论文来说，其实算是不太重要的(看了也是)。

Conclusion

The u-net architecture achieves very good performance on very different biomedical segmentation applications.

先商业自吹一下。

Thanks to data augmentation with elastic deformations, it only needs very few annotated images and has a very reasonable training time of only 10 hours on a NVidia Titan GPU (6 GB).

可以在GPU训练 10 小时。

We provide the full Caffe[6]-based implementation and the trained network. We are sure that the u-net architecture can be applied easily to many more tasks.

提供了基於 caffe 实践的开源程序码，可以轻松实现。

Concusion

这是一个如果我写出来会避不了业的结论。但是对方毕竟是很强的研究团队，在短短12页里面受益良多，接下来就是实现部分，明天见。

Reference

[0] U-net