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http://dx.doi.org/10.14372/IEMEK.2021.16.1.1

Adversarial-Mixup: Increasing Robustness to Out-of-Distribution Data and Reliability of Inference  

Gwon, Kyungpil (Daegu University)
Yo, Joonhyuk (Daegu University)
Publication Information
IEMEK Journal of Embedded Systems and Applications / v.16, no.1, 2021 , pp. 1-8 More about this Journal
Abstract
Detecting Out-of-Distribution (OOD) data is fundamentally required when Deep Neural Network (DNN) is applied to real-world AI such as autonomous driving. However, modern DNNs are quite vulnerable to the over-confidence problem even if the test data are far away from the trained data distribution. To solve the problem, this paper proposes a novel Adversarial-Mixup training method to let the DNN model be more robust by detecting OOD data effectively. Experimental results show that the proposed Adversarial-Mixup method improves the overall performance of OOD detection by 78% comparing with the State-of-the-Art methods. Furthermore, we show that the proposed method can alleviate the over-confidence problem by reducing the confidence score of OOD data than the previous methods, resulting in more reliable and robust DNNs.
Keywords
Out-of-distribution detection; Adversarial-mixup training; Deep neural networks;
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