Hi,
I am using GAN model, and I want to calculate the discriminator’s accuracy on both real samples and fake ones. Could someone helps me please?
Thank you,
Hi,
I am using GAN model, and I want to calculate the discriminator’s accuracy on both real samples and fake ones. Could someone helps me please?
Thank you,
You could calculate the accuracy as with any other classification model. Have a look at e.g. the ImageNet example.
what if I am using an unsupervised learning, how can I calculate the discriminator accuracy?
I don’t know how the accuracy calculation would work if no labels are provided.
what about the below function, is it correct:
d_acc = 0
d_acc += (fake_imgs == real_imgs).float().sum()
accuracy = 100*d_acc /len(train_dataloader)
There is another way to evaluate the discriminator in unsupervised learning?
I don’t think comparing image data directly might work well for a lot of use cases.
Especially if you are using floating point values, you would certainly need to use a small eps
value in the comparison. Also, you would have to think about what the “accuracy” really represents.
E.g. assuming you are working on a use case where a fake image should be “close” to the input image: would it matter if the generated fake image is shifted by a single pixel (potentially low “accuracy”) or would the overall “quality” of the image matter more?
How can I calculate the accuracy based on the predicted scores of the discriminator?