Code release for Transferable Curriculum for Weakly-Supervised Domain Adaptation (AAAI2019)

Related tags

Third-party APIs Wrappers TCL
Overview

TCL

Code release for Transferable Curriculum for Weakly-Supervised Domain Adaptation (AAAI2019)

Dataset

Office-31 dataset, with 0.4 label noise

Requirements

  • Python 2.7
  • PyTorch 0.3

Training

bash trainAW.sh for Amazon(0.4 label noise)-> Webcam training
bash trainAD.sh for Amazon(0.4 label noise)-> Dslr training

Citation

If you use this code for your research, please consider citing:

@inproceedings{shu2019transferable,
  title={Transferable Curriculum for Weakly-Supervised Domain Adaptation},
  author={Shu, Yang and Cao, Zhangjie and Long, Mingsheng and Wang, Jianmin},
  year={2019},
  organization={AAAI}
}

Contact

If you have any problems about our code, feel free to contact

or describe your problem in Issues.

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Comments
  • Validation set and precision

    Validation set and precision

    Appreciating for sharing your source code, two minor issues are waiting for your response.

    1. What is the role of validation set comprised of the target domain data playing?

    2. When implementing the TCL model without change any parameters, the validation precision is dropped from 60.x to 3.x, as shown below, don't know how to resolve it. image

    opened by zhyhan 2
  • Please provide noisy label files for Office 31, Office Home

    Please provide noisy label files for Office 31, Office Home

    Hi

    Thanks for the wonderful code. Can you please provide the following (1) the noisy label files for Office 31 and Office Home dataset. In the code, only the noisy label file for the Amazon dataset has been provided.

    (2) What is the noisy model used to corrupt the clean labels of the source data? Is it symmetric or pairflip? Can you please provide the code to generate the noise from our end?

    (3) Can you please provide some information regarding how the feature corruption is being done? From the paper, it is written that "For feature corruption, each image is corrupted by Gaussian blur and salt-and-pepper noise with probability p_noise." -- are you corrupting the whole image or in patches ? is hale of the images corrupted with gaussian blur and the other half with salt-and-pepper? or are both the noise models applied sequentially on top of each other? What is the value of sigma in the case of gaussian blur or the value of the threshold for the salt-and-pepper noise?

    I will be grateful if the code to generate the image corruption is provided.

    Thanks Devraj

    opened by devraj89 1
Owner
THUML @ Tsinghua University
Machine Learning Group, School of Software, Tsinghua University
THUML @ Tsinghua University
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