Establishing baselines and introducing TernaryMixOE for fine-grained out-of-distribution detection

TL;DR

This paper introduces a new theoretical framework, baseline tasks, evaluation methods, and a loss function for fine-grained out-of-distribution detection, addressing challenges in distinguishing subtle differences in hierarchical data.

Contribution

It presents a novel theoretical understanding, redefines fine-grained classification, and proposes new benchmarks and loss functions for improved OOD detection.

Findings

Established baseline tasks for fine-grained OOD detection

Developed new evaluation methods to differentiate OOD performance levels

Proposed a new loss function tailored for fine-grained OOD detection

Abstract

Machine learning models deployed in the open world may encounter observations that they were not trained to recognize, and they risk misclassifying such observations with high confidence. Therefore, it is essential that these models are able to ascertain what is in-distribution (ID) and out-of-distribution (OOD), to avoid this misclassification. In recent years, huge strides have been made in creating models that are robust to this distinction. As a result, the current state-of-the-art has reached near perfect performance on relatively coarse-grained OOD detection tasks, such as distinguishing horses from trucks, while struggling with finer-grained classification, like differentiating models of commercial aircraft. In this paper, we describe a new theoretical framework for understanding fine- and coarse-grained OOD detection, we re-conceptualize fine grained classification into a three part problem, and we propose a new baseline task for OOD models on two fine-grained hierarchical data sets, two new evaluation methods to differentiate fine- and coarse-grained OOD performance, along with a new loss function for models in this task.