Comments (3)
- discriminator 의 Input 부분 파라미터를 변경, generator 의 output feature map 크기를 변경 (이 때 upsampling 같은 방법이 필요합니다. 저는 transposed conv 를 이용해서 7x7 -> 14x14 -> 28x28 을 만들었습니다.) 데이터 로더에서 모델의 입출력 크기만큼 변경
- 1,2,3 을 넣고 4를 추론하면 anomaly score 는 높게 나올 것 같습니다. 1 로만 학습할 때 보다 데이터 분포의 분산이 커지는 건데, mri 영상을 가지고 학습해봤을 때도 동작은 했습니다. 하지만 서비스에 사용할만한 퀄리티는 보장하지 않는다고 생각합니다.
from anogan-keras.
입력이미지의 크기를 어떻게 변경할 수 있을지요??
discriminator 는 input_shape 을 변경해서 가능할 텐데...
generator_model() 에서는 어떻게 변경해야 하나요??
from anogan-keras.
generator 모델 자체를 변경 해야합니다. 모델이 출력하는 output image 크기가 입력이미지와 같게 만들면 됩니다. 고화질은 다른 GAN 테크닉이 필요할 것 같습니다.
from anogan-keras.
Related Issues (10)
- At train AnoGAN with MNIST data, Does the train set and test set all contain only normal data? HOT 1
- 결과 이미지를 보고 해석 방법?? HOT 4
- weights/generator.h5 HOT 2
- no folder of "weights" HOT 1
- envrionment HOT 1
- How to test an image is normal or abnomal? HOT 1
- Loss Function for Generator and Discriminator HOT 1
- The following error occurred after running. Do you know what happened?(运行之后出现下面的错误,有人知道是怎么回事吗?) HOT 1
- i want know how to train custom data?don't[28x28]size
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from anogan-keras.