Deep-Embedded-Validation
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Code release for Towards Accurate Model Selection in Deep Unsupervised Domain Adaptation (ICML 2019)
Deep Embedded Validation
Code release for Towards Accurate Model Selection in Deep Unsupervised Domain Adaptation (ICML 2019)
File Structure
toy.py: code for reproducing the experiments in the toy datasetdev.py: code for calculating the DEV risk
Procedure

The dev.py:get_weight can be used to get importance weight, and dev.py:get_dev_risk can be used to get validation risk.
Citation
please cite:
@InProceedings{DEV_2019_ICML,
author = {You, Kaichao and Wang, Ximei and Long, Mingsheng and Jordan, Michael I.},
title = {Towards Accurate Model Selection in Deep Unsupervised Domain Adaptation},
booktitle = {International Conference on Machine Learning (ICML)},
month = {June},
year = {2019}
}