Detecting Out-of-Distribution (OOD) inputs is crucial for improving the reliability of deep neural networks in the real-world deployment. In this paper, inspired by the inherent distribution shift between in-distribution (ID) and OOD data, we propose a novel method that leverages optimal transport to measure the distribution discrepancy between test inputs and ID prototypes. The resulting transport costs are used to quantify the individual contribution of each test input to the overall discrepancy, serving as a desirable measure for OOD detection. To address the issue that solely relying on the transport costs to ID prototypes is inadequate for identifying OOD inputs closer to ID data, we generate virtual outliers to approximate the OOD region via linear extrapolation. By combining the transport costs to ID prototypes with the costs to virtual outliers, the detection of OOD data near ID data is emphasized, thereby enhancing the distinction between ID and OOD inputs. Extensive evaluations demonstrate the superiority of our method over state-of-the-art methods.

Deep learning has shown prominence in chest radiography interpretation, which is critical in evaluating various lung and chest diseases, such as pneumonia, emphysema, and tuberculosis. Deploying machine learning model, it is important to detect out-of-distribution (OOD) inputs, which are distinct from the training data. For example, a model trained on conventional pneumonia data may not be able to diagnose COVID-19 accurately, letting alone the vast body of possible abnormalities and variation in medical imaging. Thus, a trustworthy diagnosing model should be alerted with such OOD inputs to avoid the overconfidence with samples and therefore the risk of misdiagnosis and severe medical consequences. In this paper, we study the problem of OOD detection for deep learning-based chest X-ray imaging by proposing a distance-based OOD detection method. We show that our method (1) inherits the merits of distance-based methods in being applicable to different models and training losses; and (2) adopts probabilistic score functions with fewer statistics to mitigate the potential distribution assumption on training data. We conduct extensive experiments on benchmarks to demonstrate the superiority of our method. In particular, on challenging tasks, such as Chest X-ray 14 vs. COVID-19, our method achieves 12.62% lower FPR than the state-of-the-art solutions.

The success of deep learning is largely attributed to the training over clean data. However, data is often coupled with noisy labels in practice. Learning with noisy labels is challenging because the performance of the deep neural networks (DNN) drastically degenerates, due to confirmation bias caused by the network memorization over noisy labels. To alleviate that, a recent prominent direction is on sample selection, which retrieves clean data samples from noisy samples, so as to enhance the model's robustness and tolerance to noisy labels. In this paper, we re-vamp the sample selection from the perspective of optimal transport theory and propose a novel method, called the OT-Filter. The OT-Filter provides geometrically meaningful distances and preserves distribution patterns to measure the data discrepancy, thus alleviating the confirmation bias. Extensive experiments on benchmarks, such as Clothing1M and ANIMAL-10N, show that the performance of the OT-Filter outperforms its counterparts. Meanwhile, results on benchmarks with synthetic labels, such as CIFAR-10/100, show the superiority of the OT-Filter in handling data labels of high noise.