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    So the Cross Entropy Loss really is: \[-\log \frac{\exp(x_k)}{\sum_{i=0}^{K-1}\exp(x_i)}\.

  • Then the loss function for a positive pair of examples ( i, j) is : 𝕝 l i, j = − log exp ( sim ( z i, z j) / τ) ∑ k = 1 2 N 1 [ k ≠ i] exp ( sim ( z i.
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    Driven by the intuition that good generalization.

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  • So what is the optimal case of Eq.
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  • Following the notation in [13], the contrastive loss can be defined between two augmented views (i;j) of the same example for a mini-batch of size of n, and can be written as the.
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    In the case of (1), you need to use binary cross entropy.