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jerryli27 / TwinGAN

Twin-GAN -- Unpaired Cross-Domain Image Translation with Weight-Sharing GANs
Apache License 2.0
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Cosine Distance #32

Open Zeros12 opened 5 years ago

Zeros12 commented 5 years ago

Hi, thank you for such a nice piece of work.

In Section 4.4 in your paper, it is mentioned that you have calculated the cosine distance between the flattened embedding. If we have 4 images. i.e. f(x1), f(x1'), and f(x2), f(x2') where f(x1) and f(x1') are the real images and generated images in domain X1 and f(x2) and f(x2') are the real images and generated images in domain X2. Can you please suggest did you calculate the cosine distance between f(x1) and f(x1') or between f(x1) and f(x2') or between f(x1) and f(x2) ?

Thanks in advance.

jerryli27 commented 5 years ago

Hi, thanks for your encouraging words.

Sorry I am not completely sure which part is confusing to you. The cosine distance between two normalized vectors v1 and v2 is defined as 1 - dot_product(v1, v2).

f is the trained encoder, and will thus be different for each domain. From an image, you can get the embedding by taking the output of f, flatten the vector to make it 1 dimensional, then normalize it so that it's length is 1. You do the same thing to the other input image, and you can compute cosine distance using the equation above.

Hopefully that answers your question

Zeros12 commented 5 years ago

Thanks for reply.

I am just confused about that did you calculate the cosine distance between the real images from two domains or between the generated images?

jerryli27 commented 5 years ago

Between the real images.

Zeros12 commented 5 years ago

Thank you so much.