Intelligent and Efficient Transfer Learning with Applications in Edge AI and Healthcare

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Deep learning has revolutionized machine learning in the past decade, and traditional computer vision methods based on feature crafting have been marginalized. Nevertheless, this exceptional performance comes with the price of high computation and enormous data requirements, and these hinder the use of deep learning in areas where large datasets are unavailable. Although these limitations can be partially alleviated by transfer learning, the choice of pre-trained models to be used is nontrivial. Without proper understanding of the deep learning features, transfer learning usually results in unnecessarily large models that impede deployment to edge devices and lead to expensive cloud deployments with implications for power consumption. In fact, given the myriad of pre-trained models available, it is vastly beneficial if the corresponding feature generators can be consolidated into a general repository to create accurate and efficient models for unseen tasks. In this sense, the data and computational requirements can be minimized. We think that the current paradigm of machine learning model development can be transformed by combining relevant feature generators, each being a sub-network, layer, or filters of a pre-trained neural network. As the knowledge and techniques of consolidating arbitrary pre-trained model components do not exist, this proposal aims at studying the underlying scientific and engineering possibilities to achieve this goal. In other words, Our long term vision is to build the scientific knowledge and technological know-how to enable a new paradigm of network building that is selective for components and conservative in adding them. This opens up several avenues of research, including studying methods to quantify task and network similarity, eye-gaze tracking in vision applications to quantify the importance of different components of networks in analyzing areas of interest or "attention" in images, and furthering our understanding of the way filters in different layers of networks interact. The success in implementing our goals will deliver tangible benefits in several areas. One example is in the area of edge AI for healthcare and other applications where the efficiency of networks deployed at the sensor site is paramount. Another important outcome is in green AI where reducing the energy used to run samples through a network mandates a more deliberate approach to choosing network architectures. Another area where the proposed research can impact is in applications where dense feature sets are desired, for example in radiomics applications in healthcare.