  The cost of data annotation is a substantial impediment for multi-label image
classification: in every image, every category must be labeled as present or
absent. Single positive multi-label (SPML) learning is a cost-effective
solution, where models are trained on a single positive label per image. Thus,
SPML is a more challenging domain, since it requires dealing with missing
labels. In this work, we propose a method to turn single positive data into
fully-labeled data: Pseudo Multi-Labels. Basically, a teacher network is
trained on single positive labels. Then, we treat the teacher model's
predictions on the training data as ground-truth labels to train a student
network on fully-labeled images. With this simple approach, we show that the
performance achieved by the student model approaches that of a model trained on
the actual fully-labeled images.
