Understanding the NT-Xent loss function

[Vedanshi-Shah]

Could you explain the significance of mask in the NT-Xent loss function?

mask = labels.expand(n, n).eq(labels.expand(n, n).t()).to(a2t.device)
mask_diag = mask.diag()
mask_diag = torch.diag_embed(mask_diag)
mask = mask ^ mask_diag

a2t_loss = - self.loss(a2t).masked_fill(mask, 0).diag().mean()
t2a_loss = - self.loss(t2a).masked_fill(mask, 0).diag().mean()

From what we have inferred, mask disregards the diagonal positive pairs, (i.e ( [i, i] ), but takes into account [i, j] (where i != j) positive pairs.

In the final a2t_loss calculation, we take the mean of diagonal values instead of taking the means of negative pairs. Since NT-Xent loss is supposed to account for the negative pairs similarity, how is that being calculated?

[XinhaoMei]

Softmax is applied in self.loss().在 2023年9月2日，21:40，Vedanshi Shah @.***> 写道： Could you explain the significance of mask in the NT-Xent loss function? mask = labels.expand(n, n).eq(labels.expand(n, n).t()).to(a2t.device) mask_diag = mask.diag() mask_diag = torch.diag_embed(mask_diag) mask = mask ^ mask_diag

a2t_loss = - self.loss(a2t).masked_fill(mask, 0).diag().mean() t2a_loss = - self.loss(t2a).masked_fill(mask, 0).diag().mean()

From what we have inferred, mask disregards the diagonal positive pairs, (i.e ( [i, i] ), but takes into account [i, j] (where i != j) positive pairs. In the final a2t_loss calculation, we take the mean of diagonal values instead of taking the means of negative pairs. Since NT-Xent loss is supposed to account for the negative pairs similarity, how is that being calculated?

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