The proposed taxonomy for out-of-distribution (OOD) detection can be extended to include emerging techniques like self-supervised learning (SSL) and meta-learning by creating additional subcategories that reflect their unique characteristics and methodologies.
Self-Supervised Learning (SSL) Approaches: SSL techniques can be integrated into the taxonomy under both training-driven and training-agnostic categories. For training-driven methods, SSL can be utilized to pre-train models on large unlabeled datasets, allowing them to learn robust feature representations that can enhance OOD detection capabilities. This could be categorized as "SSL-Enhanced Training-Driven OOD Detection." For training-agnostic methods, SSL can be employed to adaptively refine the model's understanding of the data distribution during inference, leading to a new subcategory called "SSL-Driven Test-Time Adaptation."
Meta-Learning Approaches: Meta-learning, or learning to learn, can also be incorporated into the taxonomy. This could be categorized under training-agnostic methods, where meta-learning techniques are used to quickly adapt to new tasks or distributions with minimal data. A new subcategory could be introduced as "Meta-Learning for OOD Detection," focusing on how models can leverage prior knowledge from related tasks to improve their OOD detection performance in novel scenarios.
Hybrid Approaches: Additionally, a hybrid category could be created to encompass methods that combine SSL and meta-learning techniques for OOD detection. This would allow for a more comprehensive understanding of how these emerging techniques can synergistically enhance OOD detection capabilities.
By incorporating these emerging techniques into the existing taxonomy, researchers and practitioners can better navigate the evolving landscape of OOD detection and identify suitable methods for specific applications.