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Existing semi-supervised learning (SSL) algorithms use a single weight to balance the loss of labeled and unlabeled examples, i.e., all unlabeled examples are equally weighted. But not all unlabeled data are equal. In this paper we study how to use a different weight for "every" unlabeled example. Manual tuning of all those weights -- as done in prior work -- is no longer possible. Instead, we adjust those weights via an algorithm based on the influence function, a measure of a model's dependency on one training example. To make the approach efficient, we propose a fast and effective approximation of the influence function. We demonstrate that this technique outperforms state-of-the-art methods on semi-supervised image and language classification tasks.

This paper introduces a large set of experiments to compare recently proposed GANs. It discusses two previously proposed measures -- inception score (IS) and Frechet Inception Distance (FID); and it proposes a new measure in the context of GAN assessment, based on precision, recall and F1. Precision (P) is measured as the fraction of generated samples with distance below a pre-defined threshold \delta; while recall (R) is measured as the fraction of inversely generated samples (from test set) with squared Euclidean distance below \delta (F1 is the usual mean between P and R). The paper argues that IS only measures precision and FIS measures both, so IS is essentially dropped as a measurement for GANs. Then the paper argues that it is important to show the mean and variance of FID and P-R-F1 measurements instead of the best values, computed over a set of random initialisations and hyper-parameter search points.