OoDAnalyzer: Interactive Analysis of Out-of-Distribution Samples

Area of Science:

• Machine Learning
• Data Visualization
• Artificial Intelligence

Background:

• Predictive model performance degrades when test data is not represented in training data, leading to out-of-distribution (OoD) samples.
• Identifying and understanding OoD samples is crucial for robust model deployment.

Purpose of the Study:

• To introduce OoDAnalyzer, a visual analysis tool for interactively identifying and explaining OoD samples.
• To improve OoD detection methods and develop an efficient grid layout algorithm for visualization.

Main Methods:

• An ensemble OoD detection method, enhanced from deep ensembles by incorporating more features and algorithms.
• A novel kNN-based grid layout algorithm, inspired by Hall's theorem, with O(kN^2) time complexity for efficient visualization of OoD samples.
• Interactive visual analysis for contextual explanation of identified OoD samples.

Main Results:

• OoDAnalyzer effectively identifies and explains OoD samples in context.
• The proposed kNN-based grid layout algorithm offers improved time complexity compared to existing methods.
• Quantitative evaluations and case studies demonstrate the tool's effectiveness and utility.

Conclusions:

• OoDAnalyzer provides a valuable approach for understanding and addressing performance degradation caused by OoD samples.
• The integration of advanced detection and efficient visualization techniques enhances the interpretability of predictive models.