Layer-Wise Activation Cluster Analysis of CNNs to Detect Out-of-Distribution Samples
| Autor: | Daniel Lehmann, Marc Ebner |
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| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783030863791 ICANN (4) |
| DOI: | 10.1007/978-3-030-86380-7_18 |
| Popis: | Convolutional neural network (CNN) models are widely used for image classification. However, CNN models are vulnerable to out-of-distribution (OoD) samples. This vulnerability makes it difficult to use CNN models in safety-critical applications (e.g., autonomous driving, medical diagnostics). OoD samples occur either naturally or in an adversarial setting. Detecting OoD samples is an active area of research. Papernot and McDaniel [43] have proposed a detection method based on applying a nearest neighbor (NN) search on the layer activations of the CNN. The result of the NN search is used to identify if a sample is in-distribution or OoD. However, a NN search is slow and memory-intensive at inference. We examine a more efficient alternative detection approach based on clustering. We have conducted experiments for CNN models trained on MNIST, SVHN, and CIFAR-10. In the experiments, we have tested our approach on naturally occurring OoD samples, and several kinds of adversarial examples. We have also compared different clustering strategies. Our results show that a clustering-based approach is suitable for detecting OoD samples. This approach is faster and more memory-efficient than a NN approach. |
| Databáze: | OpenAIRE |
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