No pseudo/hypothetical labels founded in the code.

[SupeRuier]

Hi Jordan Ash,

In your paper, the gradient embedding is from the loss between the network output and the hypothetical labels (inferred from the network output).

However, in your code, I didn't find anything about pseudo/hypothetical labels.

In the file badge_sampling.py, it seems that you directly use the true labels to guide your selection. If so, this would be an unfair comparison.

    gradEmbedding = self.get_grad_embedding(self.X[idxs_unlabeled], self.Y.numpy()[idxs_unlabeled]).numpy()

I'm not sure if I miss something. Could your show how you use the hypothetical labels in your code?

Thanks,
Rui

[SupeRuier]

Sorry I found it here in file strategy.py.

batchProbs = F.softmax(cout, dim=1).data.cpu().numpy()
maxInds = np.argmax(batchProbs,1)

Sorry for interrupting.