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Summary
| Description |
English: Hard negative mining solely based on similarity metrics may include false positives in unsupervised settings. |
| Prompt | "draw a schema that shows the difference between random negative selection, hard negative selection based on anchor similarity (may lead to false positives), and optimal hard negative selection (no false positives)" |
| Date | |
| Source | Own work |
| Author | ChatGPT |
Licensing
I, the copyright holder of this work, hereby publish it under the following license:
 
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This file is made available under the Creative Commons CC0 1.0 Universal Public Domain Dedication. |
| The person who associated a work with this deed has dedicated the work to the public domain by waiving all of their rights to the work worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law. You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission.
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This file is in the public domain because it is the work of a computer algorithm or artificial intelligence and does not contain sufficient human authorship to support a copyright claim.
 The United Kingdom (legislation) and Hong Kong (legislation) provide a limited term of copyright protection for computer-generated works of 50 years from creation.
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Legal disclaimer Most image-generating AI models were trained using works that are protected by copyright. In some cases, such models can output content with major copyrightable image elements which are identical to or derivative of the original training data, making these outputs derivative works. Accordingly, there is a risk that AI-generated media uploaded on Commons may violate the rights of the authors of the original works. See Commons:AI-generated media for additional details.
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File history
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| Date/Time | Thumbnail | Dimensions | User | Comment | |
|---|---|---|---|---|---|
| current | 14:47, 2 July 2026 | | 1,911 × 823 (1.64 MB) | Cerlonghish | Uploaded own work with UploadWizard |
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