Deep Individual Active Learning: Safeguarding against Out-of-Distribution Challenges in Neural Networks

Deep Individual Active Learning: Safeguarding against Out-of-Distribution Challenges in Neural Networks

Active learning (AL) is a paradigm focused on purposefully selecting training data to enhance a model’s performance by minimizing the need for annotated samples. Typically, strategies assume that the training pool shares the same distribution as the test set, which is not always valid in privacy-sen...

Full description

Bibliographic Details
Main Authors: Shachar Shayovitz, Koby Bibas, Meir Feder
Format: Article
Language:English
Published: MDPI AG 2024-01-01
Series:Entropy
Subjects:
active learning
universal prediction
deep active learning
individual sequences
normalized maximum likelihood
out-of-distribution
Online Access:https://www.mdpi.com/1099-4300/26/2/129

Internet

https://www.mdpi.com/1099-4300/26/2/129

Similar Items