Network Transplanting

Understand

This paper focuses on a new task, i.e., transplanting a category-and-task-specific neural network to a generic, modular network without strong supervision.

• We design an functionally interpretable structure for the generic network.
• Like building LEGO blocks, we teach the generic network a new category by directly transplanting the module corresponding to the category from a pre-trained network with a few or even without sample annotations.
• Our method incrementally adds new categories to the generic network but does not affect representations of existing categories.