Collaborative Learning with Unreliability Adaptation for Semi-Supervised Image Classification.

• Transferring the training experience among constituent networks facilitates semi-supervised image classification. • We design a collaborative learning mechanism based on unreliability adaptation among constituent networks. • We improve the complementarity of constituent networks by resisting adversarial perturbation. • The unreliability adaptation and perturbation-based regularization lead to the superior performance on multiple datasets. Constructing training goals for unlabeled data is crucial for image classification in the semi-supervised setting. Consistency regularization typically encourages a model to produce consistent predictions with the given training goals, while unreliability adaptation aims to learn the transition probabilities from model predictions to training goals, instead of enforcing their consistency. In this paper, we present a model of Collaborative learning with Unreliability Adaptation (CoUA), in which multiple constituent networks collaboratively learn with each other by adapting their predictions. Toward this end, an additional adaptation module is incorporated into each network to learn a transition probability from its own prediction to that of the paired network. Therefore, the networks can exchange training experience, without being overly sensitive to the unreliability of predictions. To further enhance the collaborative learning, each network is encouraged to produce consistent predictions with the consensus results, while being resistant to the adversarial perturbations against others. Therefore, the networks are able to mutually reinforce each other. We perform extensive experiments on multiple image classification benchmarks to verify the superiority of the co-adaptation based collaborative learning mechanism. [ABSTRACT FROM AUTHOR]