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Unlabelled batch size #39

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AlexTS1980 opened this issue Apr 24, 2021 · 5 comments
Open

Unlabelled batch size #39

AlexTS1980 opened this issue Apr 24, 2021 · 5 comments

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@AlexTS1980
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AlexTS1980 commented Apr 24, 2021

According to the paper, there's a set of labelled and unlabelled datapoints that are mixed. In the code nevertheless there are two unlabelled sets, u1 and u2. Both of them are added to the mixed set. So my question is, why are there two such sets?

@dadaplm
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dadaplm commented Oct 5, 2021

Same question.

@YU1ut
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YU1ut commented Oct 5, 2021

In Sec. 3.1 of the paper, ''For each u in the batch of unlabeled data U, we generate K augmentations.''
And K is 2 in the paper, so u1 and u2 are used.

@dadaplm
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dadaplm commented Oct 5, 2021

In Sec. 3.1 of the paper, ''For each u in the batch of unlabeled data U, we generate K augmentations.'' And K is 2 in the paper, so u1 and u2 are used.

Thank you,another question is that in the code "inputs_u" and "inputs_u2" are from the different batch ? Can I change "(inputs_u, inputs_u2), _ = unlabeled_train_iter.next()" into "inputs_u = unlabeled_train_iter.next() inputs_u2 = unlabeled_train_iter.next()" ?
Thank you so much.

@dadaplm
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dadaplm commented Oct 5, 2021

In Sec. 3.1 of the paper, ''For each u in the batch of unlabeled data U, we generate K augmentations.'' And K is 2 in the paper, so u1 and u2 are used.

I use another dataset.

@YU1ut
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YU1ut commented Oct 5, 2021

They are from the same batch but with different augmentations.
See here:
https://github.com/YU1ut/MixMatch-pytorch/blob/master/dataset/cifar10.py#L7-L14

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