SADDA

Semi-supervised Adversarial Discriminative Domain Adaptation

This repository illustrates the code of "Semi-supervised Adversarial Discriminative Domain Adaptation" - a.k.a SADDA methods on object recognition tasks on the VLCS dataset, including PASCAL VOC2007 (V), LABELME (L), CALTECH (C), and SUN (S).

(Due to limited hardware, we run this code on the Google Colab platform.).

To run this code, you need to follow those steps:

1. Download the data, with the following links: https://drive.google.com/drive/folders/1gMn4H4Hopt7-VzANwUuBokeC8HLgPB6l?usp=sharing
2. On the first cell of the *.ipynb, you need to change the link of the folder data to be suitable for your machine path.
3. After performing the first and second steps, you can run the whole notebook.
4. (Optional) You can change the hyperparameter to explore the result.
5. (Optional) You can change the name of the dataset to explore the accuracy.