Draft:Out-of-Distribution Detection
 | Draft article not currently submitted for review.
This is a draft Articles for creation (AfC) submission. It is not currently pending review. While there are no deadlines, abandoned drafts may be deleted after six months. To edit the draft click on the "Edit" tab at the top of the window. To be accepted, a draft should:
It is strongly discouraged to write about yourself, your business or employer. If you do so, you must declare it. Where to get help
How to improve a draft
You can also browse Wikipedia:Featured articles and Wikipedia:Good articles to find examples of Wikipedia's best writing on topics similar to your proposed article. Improving your odds of a speedy review To improve your odds of a faster review, tag your draft with relevant WikiProject tags using the button below. This will let reviewers know a new draft has been submitted in their area of interest. For instance, if you wrote about a female astronomer, you would want to add the Biography, Astronomy, and Women scientists tags. Editor resources
Last edited by Sadonok (talk | contribs) 5 months ago. (Update) |
Out-of-Distribution Detection is a research area within machine learning and artificial intelligence focused on identifying data inputs that significantly differ from the data distribution on which a model was trained. This task is crucial for ensuring the reliability and safety of machine learning models, particularly in safety-critical applications such as autonomous driving, medical diagnostics, and other domains where unexpected inputs could lead to catastrophic consequences.
Motivation
[edit]Methods
[edit]Posterior-based
[edit]Logit-based
[edit]Feature-based
[edit]Activation Shaping
[edit]Available Software
[edit]- OpenOOD
- Pytorch-OOD