Geo-Hgan: Unsupervised anomaly detection in geochemical data via latent space learning

Author:

Ding LiangORCID,Chen BainianORCID,Zhu YuelongORCID,Dong HaiORCID,Chan GuiyangORCID,Zhang PengchengORCID

Publisher

Elsevier BV

Reference59 articles.

1. Skip-GANomaly: Skip connected and adversarially trained encoder-decoder anomaly detection;Akay;IEEE,2019

2. GANomaly : Semi-Supervised Anomaly Detection via Adversarial Training;Akcay,2018

3. Bergmann, P., Fauser, M., Sattlegger, D., Steger, C., 2019. MVTec AD–A comprehensive real-world dataset for unsupervised anomaly detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 9592–9600.

4. Deep learning for AnomalyDetection: A survey;Chalapathy,2019

5. Constructing a high-performance self-training model based on support vector classifiers to detect gold mineralization-related geochemical anomalies for gold exploration targeting;Chen;Ore Geol. Rev.,2022

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