Several recent research work has shown that transductive learning/inference outperforms inductive learning/inference during classification problems. This has been found in few-shot learning, other metric learning works, etc. But all of these results are experimental. Is there any theoretical/mathematical reason why this is the case? Or is that a field of research for now?
If you want to look at such examples, look at section 1.1 of the paper "Transductive Information Maximization For Few-Shot Learning", here is the URL: https://arxiv.org/pdf/2008.11297.pdf
