This study adapts the Soft Teacher semi-supervised learning method, originally for object detection, to image classification, aiming to enhance accuracy with limited labeled data. STACI uses a student-teacher structure with data augmentation and EMA for prediction consistency, demonstrating the benefits of soft labels and consistency regularization in a semi-supervised context.
Here, the CIFAR-10 dataset is used for benchmarking purposes.
Code coming up soon.