This repository is deprecated, please move to transfer-caffe
This is the implementation of PAMI paper "Learning Transferable Visual Features with Very Deep Adaptation Networks". We fork the repository with version ID c6414ea from Caffe and make our modifications. The main modifications are listed as follow:
- Change the label from a single integer to a tuple, which contains the origin label and an indicator to distinguish source and target
- Add mmd layer described in the paper to neuron layers
- Add entropy loss layer described in the paper to loss layers
In models/google_net/amazon_to_webcam, we give an example model based on GoogLeNet. In this model, we insert mmd layers after inception 4d, 4e, 5a and the last fully connect layer individually. In addition, an entropy loss layer is linked after the last fully connect layer.
The bvlc_googlenet is used as the pre-trained model.
In data/office/amazon_to_webcam/*.txt, we give an example to show how to prepare the train and test data file. In this task, amazon dataset is the source domain and webcam dataset is the target domain. For training data in source domain, it's label looks like (-1, origin_label). For training data in target domain, it's label looks like (origin_label, -1). For testing data, it's label looks like (origin_label, -1). Integer -1 is used to distinguish source and target during training and testing, and origin_label is the acutal label of the image.
For semi-supervised tasks, the label of labeled target data looks like (I, origin_label). I is any positive integer (larger than the number of classes) and should be added to ignore_label of entropy loss layer.
In models/google_net/amazon_to_webcam/proto_parse.py, we write a python script to help tune the network parameters. An inception is ragarded as a unit and it's parameters are tuned together.
Besides, parameter iter_of_epoch should be set to tell mmd layer when to update data. Practically, iter_of_epoch = (source_num + target_num) / batch_size.
We use CGAL to solve Quadratic Programming problem when updating beta. Please install it before compiling this code. We add CGAL as LIBRARIES in Makefile. However, if you prefer to compile caffe with cmake, maybe you should config CGAL yourself.