Comments (4)
samet-akcay
commented on August 20, 2024
Hi, could you elaborate what you specifically meant by unreliable test set? Do you mean your labels are actually not the correct labels for their corresponding images?
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qingyuanxingsi
commented on August 20, 2024
Yeah, the labels I can get may be noisy.
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samet-akcay
commented on August 20, 2024
As you said, the evaluation metric I use in this paper requires a test set with accurate labels. This is due to the fact that the datasets used in the experiments have clean test sets. I'm note sure how one would evaluate the overall performance of a test set that has noisy labels. I would love to hear from others who might have word say about this.
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samet-akcay
commented on August 20, 2024
Iām closing this due to inactivity. Please reopen if you still encounter this issue with the latest version.
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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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- Test problem HOT 2
- Missing net.eval() in the testing code HOT 1
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