There is a recent focus on designing architectures that have an Integer Linear Programming (ILP) layer following a neural model (referred to asNeural ILP in this paper).

However, ifτ is too high, most of the unlabeled data points would not be used for consistency regularization. Based on these insights, we setτ as 0.95 in our experiments. We describe further details of the experimental setup. To train the ReMixMatch, we gradually increased the coefficient of the loss associated with the unlabeled data points, following [4]. We found that without this gradual increase, the validation loss of the ReMixMatch did not converge.

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