Name
Abstract | ||
|---|---|---|
The capability of reliably detecting out-of-distribution samples is one of the key factors in deploying a good classifier, as the test distribution always does not match with the training distribution in most real-world applications. In this work, we present a deep multi-class data description, termed as Deep-MCDD, which is effective to detect out-of-distribution (OOD) samples as well as classify in-distribution (ID) samples. Unlike the softmax classifier that only focuses on the linear decision boundary partitioning its latent space into multiple regions, our Deep-MCDD aims to find a spherical decision boundary for each class which determines whether a test sample belongs to the class or not. By integrating the concept of Gaussian discriminant analysis into deep neural networks, we propose a deep learning objective to learn class-conditional distributions that are explicitly modeled as separable Gaussian distributions. Thereby, we can define the confidence score by the distance of a test sample from each class-conditional distribution, and utilize it for identifying OOD samples. Our empirical evaluation on multi-class tabular and image datasets demonstrates that Deep-MCDD achieves the best performances in distinguishing OOD samples while showing the classification accuracy as high as the other competitors.
|
Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3394486.3403189 | KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Virtual Event
CA
USA
July, 2020 |
DocType | ISSN | ISBN |
Conference | In Proceedings of the 26th ACM SIGKDD International Conference on
Knowledge Discovery & Data Mining (pp. 1362-1370) 2020 | 978-1-4503-7998-4 |
Citations | PageRank | References |
1 | 0.39 | 10 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dongha Lee | 1 | 14 | 6.77 |
| Sehun Yu | 2 | 1 | 0.72 |
| Hwanjo Yu | 3 | 1715 | 114.02 |