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

arXiv: https://arxiv.org/abs/2303.17658 · HTML: https://mlsanity.com/paper/2303.17658/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 18 references that are in the map.
- First posted on arXiv 30 Mar 2023.

## Builds on (most cited first, 8 of 8)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks](https://mlsanity.com/paper/1610.02136/) (2016)
- [Fine-Grained Visual Classification of Aircraft](https://mlsanity.com/paper/1306.5151/) (2013)
- [Concrete Problems in AI Safety](https://mlsanity.com/paper/1606.06565/) (2016)
- [Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks](https://mlsanity.com/paper/1706.02690/) (2017)
- [Energy-based Out-of-distribution Detection](https://mlsanity.com/paper/2010.03759/) (2020)
- [Deep Anomaly Detection with Outlier Exposure](https://mlsanity.com/paper/1812.04606/) (2018)

## Abstract (arXiv)

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.