Name
Title | ||
|---|---|---|
Sniper: Few-Shot Learning For Anomaly Detection To Minimize False-Negative Rate With Ensured True-Positive Rate |
Abstract | ||
|---|---|---|
In anomaly detection systems, overlooking anomalies may result in serious incidents. Thus, when a system overlooks an anomaly, we need to update the system to never overlook the observed type of anomalies twice. There are roughly two possible approaches to solve this problem; re-training the whole system using all training data, or cascading a new specific detector for the overlooked anomaly. The first approach is the most effective solution; however, a huge computational cost and an amount of anomalous training data are required to re-train the system when it consists of a deep-learning-based anomaly detector. We focused on the latter approach and propose a training method for a cascaded specific anomaly detector using few-shot (just 1 to 3) samples. To suppress the false-negative rate of the overlooked anomaly, the proposed method works to decrease the false-positive rate under the constraint of true-positive rate equaling 1. Experimental results show that the proposed method outperformed conventional cross-entropy-based few-shot learning methods. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/icassp.2019.8683667 | 2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) |
Keywords | Field | DocType |
Few-shot learning, anomaly detection, deep learning, true-positive rate (TPR) and false-positive rate (FPR) | Training set,Anomaly detection,Pattern recognition,Computer science,Artificial intelligence,Deep learning,Detector,True positive rate | Conference |
ISSN | Citations | PageRank |
1520-6149 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Koizumi Yuma | 1 | 41 | 11.75 |
| Shin Murata | 2 | 3 | 1.80 |
| Harada Noboru | 3 | 67 | 25.07 |
| Shoichiro Saito | 4 | 13 | 2.88 |
| Hisashi Uematsu | 5 | 2 | 1.10 |