Comments (7)
@hqabcxyxz ,你好,我的最大编码距离是GNet在所有训练过的normal数据集上得出的,你可以试着把编码损失的权重增大,这么做可以有效提高异常样本的编码距离
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can you share your test code?Thank you! @dgl547437235
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@Johncheng1 ,由于是业务代码,所以不能共享,下面是我的逻辑,我训练的正样本集是A,测试的正样本集是B,测试的负样本集是C,我每训练一定的批次,就去计算在A上的最大编码距离,然后用该距离去测试B和C,超过最大编码距离则判定为NG
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@ Johncheng1,效果不错,真阳率和真阴率可以同时达到100%
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@ Johncheng1,效果不错,真阳率和真阴率可以同时达到100%
请问自己建立的模型出现这种情况该如何处理:G模型的损失值在逐步下降,而D模型的损失值基本不怎么变换,且不能区分出正样本和负样本(正样本和负样本的距离值的分布基本吻合)
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@dgl547437235 你好,我想请教以下您这里用的最大编码距离是什么距离?
因为代码的test里写的是用欧式距离,但是论文里分数用的L1范数。。。我将0设置为alnomaly,在mnist上训练了15轮之后,测试的时候发现NetG对0的重建效果也很好= =|||,分数无论用L1范数还是L2范数似乎都不能很好的找个阈值来区分正常和异常样本。
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Related Issues (20)
- 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)
- Testing
- 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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