PuzzleMix | Official PyTorch implementation of `` Puzzle Mix

by snu-mllab Jupyter Notebook Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions Vulnerabilities Install Support

kandi X-RAY | PuzzleMix Summary

PuzzleMix is a Jupyter Notebook library. PuzzleMix has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

Official PyTorch implementation of "Puzzle Mix: Exploiting Saliency and Local Statistics for Optimal Mixup" (ICML'20)

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Quality

Security

License

Reuse

Support

PuzzleMix has a low active ecosystem.

It has 140 star(s) with 14 fork(s). There are 9 watchers for this library.

It had no major release in the last 6 months.

There are 0 open issues and 9 have been closed. On average issues are closed in 14 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of PuzzleMix is current.

Quality

PuzzleMix has no bugs reported.

Security

PuzzleMix has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

PuzzleMix is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

PuzzleMix releases are not available. You will need to build from source code and install.

Installation instructions, examples and code snippets are available.

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PuzzleMix Key Features

No Key Features are available at this moment for PuzzleMix.

PuzzleMix Examples and Code Snippets

No Code Snippets are available at this moment for PuzzleMix.

Community Discussions

No Community Discussions are available at this moment for PuzzleMix.Refer to stack overflow page for discussions.

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install PuzzleMix

We provide a checkpoint of adversarial Puzzle Mix with PreActResNet18 trained on CIFAR-100. The model has 80.34% clean test accuracy and 42.89% accuracy against FGSM with 8/255 l-infinity epsilon-ball. CIFAR-100 dataset will be downloaded at [data_path], if the dataset is not exist. To test corruption robusetness, please refer to https://github.com/hendrycks/robustness to download dataset. Note that the corruption dataset should be downloaded at [data_path] with the folder name of Cifar100-C (for CIFAR100) and tiny-imagenet-200-C (for Tiny-ImageNet).

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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