Search Results for author:
Found 4 papers, 2 papers with code
Weakly Supervised Temporal Anomaly Segmentation with Dynamic Time Warping
Most recent studies on detecting and localizing temporal anomalies have mainly employed deep neural networks to learn the normal patterns of temporal data in an unsupervised manner.
Multi-Class Data Description for Out-of-distribution Detection
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.
Out-of-Distribution Image Detection Using the Normalized Compression Distance
Inspired by the method using the global average pooling on the feature maps of the convolutional neural networks, the goal of our method is to extract informative sequential patterns from the feature maps.
Deep Generative Classifier for Out-of-distribution Sample Detection
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.
