# Network Transplanting (extended abstract)

## TL;DR

This paper introduces a novel method for incrementally transplanting category-specific modules into a generic neural network without extensive supervision, enabling scalable learning of numerous categories.

## Contribution

It proposes a functionally interpretable, modular network structure and a back-distillation algorithm to facilitate category transplanting without retraining from scratch.

## Key findings

- Outperforms baseline with only 100 training samples
- Enables incremental addition of categories without affecting existing representations
- Breaks the bottleneck of simultaneous multi-category training

## Abstract

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 a 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. In this way, our method breaks the typical bottleneck of learning a net for massive tasks and categories, i.e., the requirement of collecting samples for all tasks and categories at the same time before the learning begins. Thus, we use a new distillation algorithm, namely back-distillation, to overcome specific challenges of network transplanting. Our method without training samples even outperformed the baseline with 100 training samples.