How Do I Implement Transfer Learning In Niftynet?

I'd like to perform some transfer learning using the NiftyNet stack, as my dataset of labeled images is rather small. In TensorFlow, this is possible--I can load a variety of pre-

Solution 1:

[Edit]: Here are the docs for transfer learning with NiftyNet.

This feature is currently being worked on. See here for full details.

Intended capabilities include the following:

• Command for printing all trainable variable names (with optional regular expression matching)
• Ability to randomly initialize a subset of variables, this subset is created by regex name matching
• Ability to restore (from an existing checkpoint) and continue updating a subset of the variables. If the optimization method is changed, deal with method-specific variables (e.g. momentum)
• Being able to restore (from an existing checkpoint) and freeze trained weights for the rest of the variables
• Saving all trainable variables after training
• Add configuration parameters for finetuning, variable name regex, unit tests
• A demo/tutorial
• Preprocess the checkpoints for compatibility issues
• Deal with batch norm and dropout layers (editing networks to remove batch norm variables)