A sliced-Wasserstein distance-based approach for out-of-class-distribution detection

TL;DR

This paper introduces a novel out-of-class distribution detection method using sliced-Wasserstein distance in the R-CDT space, enhancing safety and reliability in medical imaging and vision applications.

Contribution

It proposes a new out-of-class detection technique based on sliced-Wasserstein distance in the R-CDT space, addressing a key gap in existing classification methods.

Findings

Achieves better accuracy than state-of-the-art methods without out-of-class detection.

Effective on MNIST and medical image datasets.

Improves robustness and safety in real-world applications.

Abstract

There exist growing interests in intelligent systems for numerous medical imaging, image processing, and computer vision applications, such as face recognition, medical diagnosis, character recognition, and self-driving cars, among others. These applications usually require solving complex classification problems involving complex images with unknown data generative processes. In addition to recent successes of the current classification approaches relying on feature engineering and deep learning, several shortcomings of them, such as the lack of robustness, generalizability, and interpretability, have also been observed. These methods often require extensive training data, are computationally expensive, and are vulnerable to out-of-distribution samples, e.g., adversarial attacks. Recently, an accurate, data-efficient, computationally efficient, and robust transport-based classification approach has been proposed, which describes a generative model-based problem formulation and closed-form solution for a specific category of classification problems. However, all these approaches lack mechanisms to detect test samples outside the class distributions used during training. In real-world settings, where the collected training samples are unable to exhaust or cover all classes, the traditional classification schemes are unable to handle the unseen classes effectively, which is especially an important issue for safety-critical systems, such as self-driving and medical imaging diagnosis. In this work, we propose a method for detecting out-of-class distributions based on the distribution of sliced-Wasserstein distance from the Radon Cumulative Distribution Transform (R-CDT) subspace. We tested our method on the MNIST and two medical image datasets and reported better accuracy than the state-of-the-art methods without an out-of-class distribution detection procedure.