  Semi-supervised image classification, leveraging pseudo supervision and
consistency regularization, has demonstrated remarkable success. However, the
ongoing challenge lies in fully exploiting the potential of unlabeled data. To
address this, we employ information entropy neural estimation to utilize the
potential of unlabeled samples. Inspired by contrastive learning, the entropy
is estimated by maximizing a lower bound on mutual information across different
augmented views. Moreover, we theoretically analyze that the information
entropy of the posterior of an image classifier is approximated by maximizing
the likelihood function of the softmax predictions. Guided by these insights,
we optimize our model from both perspectives to ensure that the predicted
probability distribution closely aligns with the ground-truth distribution.
Given the theoretical connection to information entropy, we name our method
InfoMatch. Through extensive experiments, we show its superior performance. The
source code is available at https://github.com/kunzhan/InfoMatch.
