Closed saltoricristiano closed 3 years ago
Hi all,
thank you very much for sharing the code of your very interesting work! I'm actually facing an issue in relating what reported in your papers in equation (3)-ICLR and equation (3)-TPAMI. My doubt is: in your equation you report that L_im is obtained with the weighted sum of the entropy loss and divergence loss:
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Instead, what you do in your code here https://github.com/tim-learn/SHOT/blob/7cebb390194215823b435b0723c7b342ae62b42b/object/image_target.py#L205 is to subtract the CE loss to the entropy loss. I'll try to be clearer.Following your paper I was expecting
total_loss = entropy_loss + beta * gentropy_loss
while following what you do here https://github.com/tim-learn/SHOT/blob/7cebb390194215823b435b0723c7b342ae62b42b/object/image_target.py#L205 the resulting equation will betotal_loss = entropy_loss - beta * gentropy_loss
resulting in a minimization of the entropy while maximising the divergence component. Is there anything I'm misunderstanding?Thank you in advance for any help!
Hi, the entropy loss has a minus symbol compared with that in L_div. The code is consistent with Eq.(3).
Hi @tim-learn ,
thx for your answer, indeed I didn't see that you were computing the CE loss and then subtracting it. Closing the issue!
Hi all,
thank you very much for sharing the code of your very interesting work! I'm actually facing an issue in relating what reported in your papers in equation (3)-ICLR and equation (3)-TPAMI. My doubt is: in your equation you report that L_im is obtained with the weighted sum of the entropy loss and divergence loss:
Instead, what you do in your code here https://github.com/tim-learn/SHOT/blob/7cebb390194215823b435b0723c7b342ae62b42b/object/image_target.py#L205 is to subtract the CE loss to the entropy loss. I'll try to be clearer.Following your paper I was expecting
total_loss = entropy_loss + beta * gentropy_loss
while following what you do here https://github.com/tim-learn/SHOT/blob/7cebb390194215823b435b0723c7b342ae62b42b/object/image_target.py#L205 the resulting equation will betotal_loss = entropy_loss - beta * gentropy_loss
resulting in a minimization of the entropy while maximising the divergence component. Is there anything I'm misunderstanding?Thank you in advance for any help!