VCC-INFUSE: Towards Accurate and Efficient Selection of Unlabeled
  Examples in Semi-supervised Learning
Shijie Fang Qianhan Feng Tong Lin
Despite the progress of Semi-supervised Learning (SSL), existing methods fail
to utilize unlabeled data effectively and efficiently. Many pseudo-label-based
methods select unlabeled examples based on inaccurate confidence scores from
the classifier. Most prior work also uses all available unlabeled data without
pruning, making it difficult to handle large amounts of unlabeled data. To
address these issues, we propose two methods: Variational Confidence
Calibration (VCC) and Influence-Function-based Unlabeled Sample Elimination
(INFUSE). VCC is an universal plugin for SSL confidence calibration, using a
variational autoencoder to select more accurate pseudo labels based on three
types of consistency scores. INFUSE is a data pruning method that constructs a
core dataset of unlabeled examples under SSL. Our methods are effective in
multiple datasets and settings, reducing classification errors rates and saving
training time. Together, VCC-INFUSE reduces the error rate of FlexMatch on the
CIFAR-100 dataset by 1.08% while saving nearly half of the training time.