Temporally Precise Action Spotting in Soccer Videos Using Dense Detection Anchors

A major problem of training deep learning image classifiers is the limited availability of domain-specific labeled datasets. This problem is particularly pressing in medicine due to the cost and expertise needed for annotation. This paper proposes a classifier training strategy to reduce model misclassification by 1) using weakly supervised learning to learn class confusion, 2) creating a new class with synthetic training data highlighting the confusion, and 3) training with the expanded training data and using transfer learning. We tested our strategy using the open medical (Kvasir) and non-medical (CIFAR10) datasets. The proposed training strategy improves classification accuracy, precision, and recall when applied to a small subset of the data for training.