Anomaly detection for images
Iterative energy-based projection on a normal data manifold for anomaly localization ICLR 2020: https://openreview.net/forum?id=HJx81ySKwr
reference:https://qiita.com/kogepan102/items/122b2862ad5a51180656
Anomaly detection for images
License: MIT License
Anomaly detection for images
Iterative energy-based projection on a normal data manifold for anomaly localization ICLR 2020: https://openreview.net/forum?id=HJx81ySKwr
reference:https://qiita.com/kogepan102/items/122b2862ad5a51180656
I couldn't get as much accuracy as the results in the paper, is there something wrong with the code? For example, are the hyperparameters different, or is the architecture different from the one in the paper?
it seems that it doesn't work well and have a few bugs? such as the checkpoint_dir and the loss_function is not appropriate.
Can you tell me how to use this function in EBM?
x_t = x_t - eps * x_grad * (x_t - recon_x) ** 2
eps is not defined in train.py
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