google-research / adamatch

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Updated in 2023.

adamatch's Issues

Security Policy violation Binary Artifacts

This issue was automatically created by Allstar.

Security Policy Violation
Project is out of compliance with Binary Artifacts policy: binaries present in source code

Rule Description
Binary Artifacts are an increased security risk in your repository. Binary artifacts cannot be reviewed, allowing the introduction of possibly obsolete or maliciously subverted executables. For more information see the Security Scorecards Documentation for Binary Artifacts.

Remediation Steps
To remediate, remove the generated executable artifacts from the repository.

First 10 Artifacts Found

• fully_supervised/pycache/init.cpython-38.pyc
• fully_supervised/libml/pycache/init.cpython-38.pyc
• fully_supervised/libml/pycache/models.cpython-38.pyc
• fully_supervised/libml/pycache/train.cpython-38.pyc
• fully_supervised/libml/pycache/util.cpython-38.pyc
• fully_supervised/libml/augment/pycache/init.cpython-38.pyc
• fully_supervised/libml/augment/pycache/augment.cpython-38.pyc
• fully_supervised/libml/augment/pycache/core.cpython-38.pyc
• fully_supervised/libml/augment/pycache/ctaugment.cpython-38.pyc
• fully_supervised/libml/data/pycache/init.cpython-38.pyc
• Run a Scorecards scan to see full list.

Additional Information
This policy is drawn from Security Scorecards, which is a tool that scores a project's adherence to security best practices. You may wish to run a Scorecards scan directly on this repository for more details.

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About Batch Normalization Statistics When using Domain Adaptation Setting

Hi, thanks for your wonderful work. From the paper, the batch normalization statistics updates for combined source and target images, and keep fixed when computing source images for the second time. However, from https://github.com/google-research/adamatch/blob/main/domain_adaptation/adamatch.py#L63 and L67, the model keeps training states. I am not familiar with Jax. Would you mind giving some explanations?

The settings is not consistent with the standards of ssl?

In the SSL setting, the target data is not the same as test data, although they are in the same distribution. But in your paper, the target data is used as test data. overall, the setting of UDA is not the same as SSL.

Possible to give a mention?

Hi @rolloff.

Just when this paper had come out, I worked on a blog post: https://keras.io/examples/vision/adamatch/. It's accompanied by this repository: https://github.com/sayakpaul/AdaMatch-TF.

Would be great if you could include it inside the repository, it might be helpful for folks that work with TensorFlow and Keras.

Why are your settings not in accordance with the standards of ssl, UDA and SSDA?

Thank you for your excellent work. I'm curious why you didn't keep the same settings as before to experiment? For example, in the SSL field, the commonly used data sets are cifar10 and cifar100, as well as SVHN and STL-10. The commonly used in UDA are office-home and office31. The commonly used in SSDA are Domainnet and Officehome. If you change the settings, it is difficult to compare fairly with previous work.

Hyperparameters of Office31 dataset

Hi,
Can you provide the office31 hyperparameters for training with UDA.