Some transfer learning algorithms modified from microsoft/UDA .

Introduction

Some transfer learning and partial transfer learning algorithms were implemented using PyTorch. There are 5 algorithms including ERM, CORAL, DAN (MMD), DANN, and IWAN (partial transfer learning) in total. Note that the author mainly researched fault diagnosis, so the default dataset is the DDS dataset constructed by ourselves. See more information in the next section.

Usage

You can directly modify the params in the function parse_args in main.py, then run main.py. You can also run main.py in terminal like: python main.py --num-classes=5 --src='20R-0HP'.

Using your own dataset

You should split your data into a train/test set as follows:
  ├─x_train.pt
  ├─y_train.pt
  ├─x_test.pt
  └─y_test.pt
Then write the data loader file refering to the file in dds.py under datasets folder.
The file task.py under the folder tasks are set to generate transfer tasks, and our DDS dataset consists of 9 kinds of operation condition:

condition	20R-0HP	20R-4HP	20R-8HP	30R-0HP	30R-4HP	30R-8HP	40R-0HP	40R-4HP	40R-8HP
number	0	1	2	3	4	5	6	7	8

So there is a total of 72 transfer tasks. You can refer to the aforementioned file to generate your tasks. The shape of the 1-dimensional data is 1024 and the shape of the 2-dimensional data is 64x64. If you use the 2-dimensional data and use resnet18 as the backbone, please modify the _cnn_fidm parameter to 512 in line 16 in file base_model.py under the folder models.