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Published 20 hours ago • verified publisher icon sulaimanvesal
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[IEEE-TMI 2021] This is our PyTorch implementation for Adapt Everywhere paper on unsupervised domain adaptation using entropy and point-cloud paper.

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How to visualize the entropy maps

Dear author, How to visualize the entropy maps? Like this: ![image](https://github.com/sulaimanvesal/PointCloudUDA/assets/28928231/9d30027e-aad4-4aa7-9363-dfc3bb6a17d6) I can obtain the entropy maps, but I don't know how to visualize them.

TshellT avatar TshellT

Sourcery refactored master branch

Branch `master` refactored by [Sourcery](https://sourcery.ai/github/). If you're happy with these changes, merge this Pull Request using the *Squash and merge* strategy. See our documentation [here](https://docs.sourcery.ai/GitHub/Using-Sourcery-for-GitHub/). Run Sourcery locally Reduce the...

sourcery-ai[bot] avatar sourcery-ai[bot]

image input

1 comment

It is a 2D point-cloud regression,why the input image is R( w x h x 3)?

wangm-ting avatar wangm-ting

How to visualize segmentation results “soft-segmentation map”

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wangm-ting avatar wangm-ting

Data set

1 comment

Where can I download the processed data about the MS-CMRSeg dataset?

AprilT0621 avatar AprilT0621

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verified publisher icon sulaimanvesal

Metadata

[IEEE-TMI 2021] This is our PyTorch implementation for Adapt Everywhere paper on unsupervised domain adaptation using entropy and point-cloud paper.

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