Comments (3)
- The precision and recall of G are printed to console if you run this script. The performance of G learned with Omniglot-bg and tested with Omniglot-eval or MNIST are available in Appendix Table 7 and 13 of the paper.
- The sampler does not aim to solve the imbalance problem completely. The sampler only makes the imbalance problem less severe to make sure the number of similar pairs in a mini-batch is not zero. If the imbalance is a concern in your case, a simple option is to calibrate the threshold of binarization after the G is learned.
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Can you please tell me how you converted [sij=0]P(Yi|xi,theta)PYj|xj,theta) to (1-sij)log(1-f(xi,theta)Tf(fxj,theta))
It seems like P(Yi|xi,theta)PYj|xj,theta) ->1-f(xi,theta)Tf(fxj,theta) for no reason.
It conflicts with the theory when 2 classes are differents. Your loss function has a positive class =1 when 2 samples have the same predictions. It means the higher overlap, the loss should be lower. In your case, the lower of overlap, the loss is lower.
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Hi linhlt-it-ee, your question was answered in another thread.
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