f-CLSWGAN
Introduction
This work follows the idea from Yongqin Xian, Tobias Lorenz, Bernt Schiele, Zeynep Akata. "Feature Generating Networks for Zero-Shot Learning." CVPR (2018).
I did not copy any codes directly, except the calc_gradient_penalty function (about 15 lines) in train.py.
All of the work is developed by myself for about 8 hours.
The net sturcture is pretty similar to f-CLSWGAN. The trianing setting is almost the same as it.
Train the net.
Environment
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Python: 3.7,
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PyTorch: 1.2,
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scipy.
Prepare dataset
Firstly, download datasets from https://datasets.d2.mpi-inf.mpg.de/xian/xlsa17.zip, then edit the 'res_path' and 'att_path' in args.py to point to your dataset location.
Start the training.
Use 'python main.py' to start the training .
BTW
The datasets are 2048-d extracted feature maps from resnet-101.