Training Neural Networks to Produce Compatible Features
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This paper makes a first step towards compatible and hence reusable network components. Rather than training networks for different tasks independently, we adapt the training process to produce network components that are compatible across tasks. We propose and compare several different approaches to accomplish compatibility. Our experiments on CIFAR-10 show that: (i) we can train networks to produce compatible features, without degrading task accuracy compared to training networks independently; (ii) the degree of compatibility is highly dependent on where we split the network into a feature extractor and a classification head; (iii) random initialization has a large effect on compatibility; (iv) we can train incrementally: given previously trained components, we can train new ones which are also compatible with them. This work is part of a larger goal to increase network reusability: we envision that compatibility will enable solving new tasks by mixing and matching suitable components.
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