Semi-Supervised Domain Adaptation via Selective Pseudo Labeling and Progressive Self-Training (ICPR 2020)

Acknowledgement

The implementation is built on the pytorch implementation of SSDA_MME, which is the baseline model of our proposed SSDA scheme.

Prerequisites

• CUDA
• Python 3.6+
• PyTorch 0.4.0+
• Pillow, numpy, tqdm

Dataset Structure

dataset---
     |
   multi---
     |   |
     |  real
     |  clipart
     |  sketch
     |  painting
   office_home---
     |   |
     |  Art
     |  Clipart
     |  Product
     |  Real
   office---
     |   |
     |  amazon
     |  dslr
     |  webcam

Example

Training & Validation

• DomainNet (clipart, painting, real, sketch)

The proposed SSDA scheme consists of four stages.

An example for running a DA scenario is given as follows.

python s1_trainval_baseline.py --net resnet34 --source real --target clipart --num 3
python s2_eval_and_save_features.py --net resnet34 --source real --target clipart --num 3
python s3_selective_pseudo_labeling.py --net resnet34 --source real --target clipart --num 3
python s4_trainval_prog_self_training.py --net resnet34 --source real --target clipart --num 3

Or you can run the above stages by simply executing the bash script as follows.

bash trainval_SSDA.sh