Anomaly detection on the UC Berkeley milling data set using a disentangled-variational-autoencoder (beta-VAE). Replication of results as described in article "Self-Supervised Learning for Tool Wear Monitoring with a Disentangled-Variational-Autoencoder"
License: MIT License
Hello, I used your code to search for more than 200 models. During training, they have good accuracy of verification set (about 0.86), but in the subsequent calculation of pr-AUC, their performance (0.1-0.3) is far from the best model you provided, and PR curve and violin plots also look very bad. I want to know if there is any other work besides changing the search times to find the effect like the model you provided. I'm sorry for my poor English. I wonder if you can understand me
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