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ml-recall

#label-smoothing

1 question from Generative Adversarial Networks

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Question 1: Short answer

mediumAI-checkedGenerative Adversarial Networks

With one-sided label smoothing, the discriminator's loss on real images uses target 0.9 instead of 1:

labels = torch.ones(batch_size) * 0.9
loss = nn.BCEWithLogitsLoss()(D_out.squeeze(), labels)

For a single real image, which logit D_out minimizes this loss? Give the value (natural log).

a number, 3 decimals