Notes on Transfer Learning in Caffe

This is a very straightforward practical approach:

One trick I’ve learned from somewhere (can’t find the link, unfortunately), which is a break from the above tutorial, is to simply reduce the base learning rate by an order of magnitude when transferring, while simultaneously seting the “new” layers to have their lr_mult an order of magnitude higher than the rest of the network

so, to initially train:

  1. learning rate =.01
  2. each layer's lr_rate = 1

to transfer learn:

  1. learning rate = .001
  2. each previous layer's lr_rate = 1
  3. new layers lr_rate = 10

This saves a lot of editing of the files and allows for small amounts of adjustment to existing layers, while focusing the bulk of the learning on the newer layers, yet still resisting to overfitting small datasets.

This is pretty informative:
And I think this is the original TL paper as far as I remember:

On Stain Normalization in Deep Learning

Just wanted to take a moment and share some quick stain normalization type experimental results. We have a trained in-house nuclei segmentation model which works fairly well when the test images have similar stain presentation properties, but when new datasets arrive which are notably different we tend to see a decreased classifier performance.

Here we look at one of these images and ways of improving classifier robustness.

Continue reading On Stain Normalization in Deep Learning