  Training with fewer annotations is a key issue for applying deep models to
various practical domains. To date, semi-supervised learning has achieved great
success in training with few annotations. However, confirmation bias increases
dramatically as the number of annotations decreases making it difficult to
continue reducing the number of annotations. Based on the observation that the
quality of pseudo-labels early in semi-supervised training plays an important
role in mitigating confirmation bias, in this paper we propose an active
self-semi-supervised learning (AS3L) framework. AS3L bootstraps semi-supervised
models with prior pseudo-labels (PPL), where PPL is obtained by label
propagation over self-supervised features. We illustrate that the accuracy of
PPL is not only affected by the quality of features, but also by the selection
of the labeled samples. We develop active learning and label propagation
strategies to obtain better PPL. Consequently, our framework can significantly
improve the performance of models in the case of few annotations while reducing
the training time. Experiments on four semi-supervised learning benchmarks
demonstrate the effectiveness of the proposed methods. Our method outperforms
the baseline method by an average of 7\% on the four datasets and outperforms
the baseline method in accuracy while taking about 1/3 of the training time.
