URL | Universal Representation Learning from Multiple Domains

by VICO-UoE Python Version: Current License: MIT

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

kandi X-RAY | URL Summary

URL is a Python library. URL has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However URL build file is not available. You can download it from GitHub.

Universal Representation Learning from Multiple Domains for Few-shot Classification - ICCV 2021, Cross-domain Few-shot Learning with Task-specific Adapters - CVPR 2022

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Quality

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License

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Support

URL has a low active ecosystem.

It has 95 star(s) with 15 fork(s). There are 8 watchers for this library.

It had no major release in the last 6 months.

There are 3 open issues and 14 have been closed. On average issues are closed in 21 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of URL is current.

Quality

URL has no bugs reported.

Security

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

License

URL 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

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

URL has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions, examples and code snippets are available.

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URL Key Features

No Key Features are available at this moment for URL.

URL Examples and Code Snippets

No Code Snippets are available at this moment for URL.

Community Discussions

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

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

Vulnerabilities

No vulnerabilities reported

Install URL

Clone or download this repository.
Configure Meta-Dataset: Follow the "User instructions" in the Meta-Dataset repository for "Installation" and "Downloading and converting datasets". Edit ./meta-dataset/data/reader.py in the meta-dataset repository to change dataset = dataset.batch(batch_size, drop_remainder=False) to dataset = dataset.batch(batch_size, drop_remainder=True). (The code can run with drop_remainder=False, but in our work, we drop the remainder such that we will not use very small batch for some domains and we recommend to drop the remainder for reproducing our methods.) To test unseen domain (out-of-domain) performance on additional datasets, i.e. MNIST, CIFAR-10 and CIFAR-100, follow the installation instruction in the CNAPs repository to get these datasets.

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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