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EndlessSora avatar EndlessSora commented on August 29, 2024

tmp = (recon_freq - real_freq) ** 2

Thanks for your interest. We performed the square operation in Line 89. We use the squared Euclidean distance. You can consider printing each tensor out to understand the code.

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IItaly avatar IItaly commented on August 29, 2024

tmp = (recon_freq - real_freq) ** 2

Thanks for your interest. We performed the square operation in Line 89. We use the squared Euclidean distance. You can consider printing each tensor out to understand the code.

Thank you! Can I regard that (recon_freq - real_freq) as the Euclidean distance?

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EndlessSora avatar EndlessSora commented on August 29, 2024

No. You can regard the square root of freq_distance in Line 90 as the Euclidean distance. We apply the squared Euclidean distance to ensure a smooth gradient.

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IItaly avatar IItaly commented on August 29, 2024

No. You can regard the square root of freq_distance in Line 90 as the Euclidean distance. We apply the squared Euclidean distance to ensure a smooth gradient.

Thanks for your reply. I don't think I understand what tmp[..., 0], tmp[..., 1] is doing.
And If I want to make the distance bigger in other research area whether I could refer to this loss? like 1/FFLoss
One problem I face is that the loss does not decrease when it set to 1/FFLoss.
Would you have any ideas?

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EndlessSora avatar EndlessSora commented on August 29, 2024

If you would like the frequency distance to be larger as the model is trained, you may try to apply -FFLoss as your objective function.

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