Network Transplanting
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This paper focuses on a novel problem, i.e., transplanting a category-and-task-specific neural network to a generic, distributed network without strong supervision. Like playing LEGO blocks, incrementally constructing a generic network by asynchronously merging specific neural networks is a crucial bottleneck for deep learning. Suppose that the pre-trained specific network contains a module f to extract features of the target category, and the generic network has a module g for a target task, which is trained using other categories except for the target category. Instead of using numerous training samples to teach the generic network a new category, we aim to learn a small adapter module to connect f and g to accomplish the task on a target category in a weakly-supervised manner. The core challenge is to efficiently learn feature projections between the two connected modules. We propose a new distillation algorithm, which exhibited superior performance. Our method without training samples even significantly outperformed the baseline with 100 training samples.
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