Contents
Why deep supervision?
The advantage of such deep supervision is evident: (1) for small training data and relatively shallower networks, deep supervision functions as a strong “regularization” for classification accuracy and learned features; (2) for large training data and deeper networks deep supervision makes it convenient to exploit the …
What is intermediate supervision?
ds2268 commented on Oct 4, 2017. Intermediate supervision is implemented such that at the end of each stage you have loss to be computed instead of just at the end.
Which is an application of a deep supervision scheme?
We present a deep supervision scheme with intermediate concepts for deep neural networks. One application of our deep supervision is 3D object structure inference which is linked to recent advances including reconstruction, alignment and pose estimation. We review related work on these problems in the following: Multi-task Learning.
How to train deeper convolutional networks with deep supervision?
They show that this deep supervision yields an improved convergence rate, but their experiments are limited with a not-so-deep network structure. In this work, to train deeper networks more efficiently, we also adopt the idea of adding auxiliary classifiers after some of the intermediate convolutional layers.
How is deep supervision used in semantic segmentation?
The papers says that deep supervision enables: “the model to operate in two modes: 1) accurate mode wherein the outputs from all segmentation branches are averaged; 2) fast mode wherein the final segmentation map is selected from only one of the segmentation branches, the choice of which determines the extent of model pruning and speed gain”
What’s the difference between UNET and deep supervision?
The second difference with Unet is the use of deep supervision. The papers says that deep supervision enables: