Comments (4)
I have the same confusion as well, it seems that the classifier of the NetD are never modified and just stay as what it was initialized?
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hello,have you run the code successfully ?
from ganomaly.
Hi @lzwhard and @gdjmck,
As you pointed out, the previous version had some issues with the discriminator training. The updated code trains the discriminator in an adversarial setting. The feature matching loss |f(x) - f(x')| from discriminator is added to the generator training, which overall improves the performance of the model. Please check out the latest version, and let me know if you have any concerns.
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I'm closing this issue due to inactivity. If needed, the discussion could be continued in #28.
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Related Issues (20)
- how can i balance the model HOT 7
- how can i see the result of test?? HOT 1
- How to distinguish a mnist_pic is normal or anomaly? HOT 17
- Sudden increase of err_d HOT 1
- Changes for Windows10
- Some qutestions about testing
- can it display where is the anomaly when inference? HOT 1
- what is the difference between fixed_fakes_*.png and fakes.png
- Is the testing function missing self.netg.eval? HOT 5
- AttributeError: module 'torch._C' has no attribute '_cuda_setDevice' HOT 4
- What's the performance of the model in terms of custom dataset?
- Can this code be used on sequence data? HOT 1
- How to use ganomaly neural network for single-channel data training
- Program stucks when the input image size is 224x224 HOT 2
- Experiment(To replicate the results in the paper for MNIST and CIFAR10 datasets)
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- Possible mode collapse HOT 1
- Test problem HOT 2
- Missing net.eval() in the testing code HOT 1
- ROC is changed every time
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