OoDAnalyzer: Interactive Analysis of Out-of-Distribution Samples

by   Changjian Chen, et al.

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(kN^2) time complexity, faster than the grid layout algorithm with overall best performance but O(N^3) time complexity. Quantitative evaluation and case studies were performed on several datasets to demonstrate the effectiveness and usefulness of OoDAnalyzer.



page 3

page 5

page 6

page 7

page 8

page 12

page 13

page 15


Fast and Work-Optimal Parallel Algorithms for Predicate Detection

Recently, the predicate detection problem was shown to be in the paralle...

ORC Layout: Adaptive GUI Layout with OR-Constraints

We propose a novel approach for constraint-based graphical user interfac...

Warehouse Layout Method Based on Ant Colony and Backtracking Algorithm

Warehouse is one of the important aspects of a company. Therefore, it is...

Verifying Asymptotic Time Complexity of Imperative Programs in Isabelle

We present a framework in Isabelle for verifying asymptotic time complex...

3D IC optimal layout design. A parallel and distributed topological approach

The task of 3D ICs layout design involves the assembly of millions of co...

Scalable Comparative Visualization of Ensembles of Call Graphs

Optimizing the performance of large-scale parallel codes is critical for...

Does the Layout Really Matter? A Study on Visual Model Accuracy Estimation

In visual interactive labeling, users iteratively assign labels to data ...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.