Appearance-Based Curriculum for Semi-Supervised Learning with Multi-Angle Unlabeled Data.

We propose an appearance-based curriculum (ABC) for a semi-supervised learning scenario where labeled images taken from limited angles and unlabeled ones taken from various angles are available for training. A common approach to semi-supervised learning relies on pseudo-labeling and data augmentation, but it struggles with large visual variations that cannot be covered by data augmentation. To solve this problem, ABC incrementally expands the pool of unlabeled images fed to a base semi-supervised learner so that newly added data are the ones most similar to those already in the pool. This way, the learner can assign pseudo-labels to the new data with high accuracy, keeping the quality of pseudo-labels higher than that when all the unlabeled data are processed at once, as customarily done in existing semi-supervised learning methods. We conducted extensive experiments and confirmed that our method outperforms the state-of-the-art semi-supervised learning methods in our scenario.