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 harness 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
\textit{InfoMatch}. Through extensive experiments, we show its superior
performance.

利用伪监督和一致性正则化的半监督图像分类方法，在最大化增强视图之间的互信息下限时，通过信息熵神经估计利用未标记样本的潜力，从理论上分析出图像分类器的信息熵通过最大化 softmax 预测的似然函数近似估计。在这些见解的指导下，我们从两个角度优化我们的模型，以确保预测的概率分布与真实分布紧密一致，并通过大量实验证明其卓越性能。