The key idea of CL-BMF is to pass the knowledge from the low-complexity model to the high-complexity model (i.e. co-learning) to reduce its training cost.
once the low-complexity model is accurately built based on a small number of training samples, the high-complexity model can be trained by borrowing the information from the lowcomplexity model, instead of relying on the expensive training data only.