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Code release for Towards Accurate Model Selection in Deep Unsupervised Domain Adaptation (ICML 2019)
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README.md

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 dataset
  • dev.py: code for calculating the DEV risk

Procedure

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}
}

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