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Most uses of machine learning today involve training a model from scratch fora particular task, or sometimes starting with a model pretrained on a relatedtask and then fine-tuning on a downstream task. Both approaches offer limitedknowledge transfer between different tasks, time-consuming human-drivencustomization to individual tasks and high computational costs especially whenstarting from randomly initialized models. We propose a method that uses thelayers of a pretrained deep neural network as building blocks to construct anML system that can jointly solve an arbitrary number of tasks. The resultingsystem can leverage cross tasks knowledge transfer, while being immune fromcommon drawbacks of multitask approaches such as catastrophic forgetting,gradients interference and negative transfer. We define an evolutionaryapproach designed to jointly select the prior knowledge relevant for each task,choose the subset of the model parameters to train and dynamically auto-tuneits hyperparameters. Furthermore, a novel scale control method is employed toachieve quality/size trade-offs that outperform common fine-tuning techniques.Compared with standard fine-tuning on a benchmark of 10 diverse imageclassification tasks, the proposed model improves the average accuracy by 2.39%while using 47% less parameters per task.

muNet: Evolving Pretrained Deep Neural Networks into Scalable Auto-tuning Multitask Systems