Artificial Intelligence

    

Improved Memory-guided Normality with Specialized Training Techniques of Deep SVDD

Authors: Xie Lei

Deep learning techniques have shown remarkable success in various tasks, including feature learning, representation learning, and data reconstruction. Autoencoders, a subset of neural networks, are particularly powerful in capturing data patterns and generating meaningful representations. This paper presents an investigation into the use of combination with Deep SVDD and memory modules.

Comments: 2 Pages.

Download: PDF

Submission history

[v1] 2023-08-12 13:44:31

Unique-IP document downloads: 210 times

Vixra.org is a pre-print repository rather than a journal. Articles hosted may not yet have been verified by peer-review and should be treated as preliminary. In particular, anything that appears to include financial or legal advice or proposed medical treatments should be treated with due caution. Vixra.org will not be responsible for any consequences of actions that result from any form of use of any documents on this website.

Add your own feedback and questions here:
You are equally welcome to be positive or negative about any paper but please be polite. If you are being critical you must mention at least one specific error, otherwise your comment will be deleted as unhelpful.

comments powered by Disqus