# OoDAnalyzer: Interactive Analysis of Out-of-Distribution Samples

arXiv: https://arxiv.org/abs/2002.03103 · HTML: https://mlsanity.com/paper/2002.03103/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Feb 2020.

## Built on by (most cited first, 30 of 33)

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- [DeepLens: Interactive Out-of-distribution Data Detection in NLP Models](https://mlsanity.com/paper/2303.01577/) (2023)
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- [ScatterUQ: Interactive Uncertainty Visualizations for Multiclass Deep Learning Problems](https://mlsanity.com/paper/2308.04588/) (2023)
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- [MiMICRI: Towards Domain-centered Counterfactual Explanations of Cardiovascular Image Classification Models](https://mlsanity.com/paper/2404.16174/) (2024)
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- [Interactive Hybrid Rice Breeding with Parametric Dual Projection](https://mlsanity.com/paper/2507.11848/) (2025)
- [VisMoDAl: Visual Analytics for Evaluating and Improving Corruption Robustness of Vision-Language Models](https://mlsanity.com/paper/2509.14571/) (2025)
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- [Exploring How Anomalous Model Input and Output Alerts Affect Decision-Making in Healthcare](https://mlsanity.com/paper/2204.13194/) (2022)

## Abstract (arXiv)

One major cause of performance degradation in predictive models is that the test samples are not well covered by the training data. Such not well-represented samples are called OoD samples. In this paper, we propose OoDAnalyzer, a visual analysis approach for interactively identifying OoD samples and explaining them in context. Our approach integrates an ensemble OoD detection method and a grid-based visualization. The detection method is improved from deep ensembles by combining more features with algorithms in the same family. To better analyze and understand the OoD samples in context, we have developed a novel kNN-based grid layout algorithm motivated by Hall's theorem. The algorithm approximates the optimal layout and has O(kN2) time complexity, faster than the grid layout algorithm with overall best performance but O(N3) time complexity. Quantitative evaluation and case studies were performed on several datasets to demonstrate the effectiveness and usefulness of OoDAnalyzer.