  Temporal Ensembling is a semi-supervised approach that allows training deep
neural network models with a small number of labeled images. In this paper, we
present our preliminary study on the effect of intraclass variability on
temporal ensembling, with a focus on seed size and seed type, respectively.
Through our experiments we find that (a) there is a significant drop in
accuracy with datasets that offer high intraclass variability, (b) more seed
images offer consistently higher accuracy across the datasets, and (c) seed
type indeed has an impact on the overall efficiency, where it produces a
spectrum of accuracy both lower and higher. Additionally, based on our
experiments, we also find KMNIST to be a competitive baseline for temporal
ensembling.
