Network Transplanting

By Quanshi Zhang, Yu Yang, Ying Nian Wu, Song-Chun Zhu. 2018

5 citations recorded by the source corpus.

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. 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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

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In Artificial Intelligence

In Computer Vision and Pattern Recognition

Citations in the corpus

Interpreting CNNs via Decision Trees (2018, background): “[42] learned functionally interpretable, modular structures for neural networks via network transplanting.”