document transfer learning

Author: bjarthur

README.md

@@ -28,6 +28,7 @@ Table of Contents
       * [Searching Hyperparameters](#searching-hyperparameters)
       * [Examining Errors](#examining-errors)
       * [Ensemble Models](#ensemble-models)
+      * [Transfer Learning](#transfer-learning)
       * [Testing Densely](#testing-densely)
       * [Discovering Novel Sounds](#discovering-novel-sounds)
       * [Overlapped Classes](#overlapped-classes)
@@ -1502,6 +1503,36 @@ the thresholds file of one of the constituent models, and (3) calculate the
 Manually copy this file into the newly created ensemble folder, and use
 it whenever classifying recordings with this ensemble model.
 
+## Transfer Learning ##
+
+Manually annotation can be a lot of work.  Fortunately, the effort spent doing
+so can be reduced by leveraging someone else's work.  Let's say your colleague
+Alice has trained a model to do a task similar to what you need.  You can take
+her model, keep the first few layers intact with their learned weights, replace
+the last couple of layers with randomly initialized ones of your own, and then
+iteratively train and annotate as described above.  The features in the early
+layers will already be quite rich, and so the new latter layers will not need
+as much ground truth data to learn your task.  Moreover, if your colleagues Bob
+and Carol also have similar trained models, you can combine all three in a
+similar fashion.
+
+SongExplorer comes with an architecture plugin (see [Customizing with
+Plug-ins](#customizing-with-plug-ins) for details on how plugins work) called
+"ensemble-transfer" that makes all this easy.  Modify the `architecture_plugin`
+variable in your "configuration.py" file to be "ensemble-transfer".  Then in
+the SongExplorer GUI specify (1) the checkpoint(s) of the pretrained model(s)
+you want to use, (2) whether you want to update the weights of the pretrained
+model when you train with your data (or just the new layers), (3) how many of
+the layers of the pretrained model(s) you want to use, (4) how many new
+convolutional layers you want to add (for each layer: kernel time size x kernel
+frequency size x num. features; e.g. "5x5x32,10x10x64"), (5) how many new dense
+layers you want to add (for each layer: num. units; e.g. "128,32,8"), and (6)
+the dropout rate (e.g. 50).  Then iteratively train and fix mistakes as before.
+
+Note that the pretrained models do *not* necessarily have to use the sampling
+rate-- the "ensemble-transfer" plugin will automatically insert a resampling
+layer if necessary.
+
 ## Discovering Novel Sounds ##
 
 After amassing a sizeable amount of ground truth one might wonder whether one