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

在过去的十年里，深度学习使机器学习发生了革命性的变化，基于特征制作的传统计算机视觉方法已经被边缘化。然而，这种卓越的性能是以高计算量和巨大的数据需求为代价的，这阻碍了深度学习在大型数据集不可用的领域的使用。虽然转移学习可以部分缓解这些限制，但选择要使用的预先训练的模型并不是一件容易的事情。如果不正确了解深度学习功能，迁移学习通常会导致不必要的大型模型，从而阻碍部署到边缘设备，并导致昂贵的云部署，从而影响功耗。事实上，考虑到可用的无数预先训练的模型，如果相应的特征生成器可以合并到通用储存库中，为看不见的任务创建准确而高效的模型，这将是非常有益的。从这个意义上说，可以将数据和计算要求降到最低。我们认为，当前机器学习模型开发的范例可以通过组合相关的特征生成器来转换，每个特征生成器都是预先训练的神经网络的子网络、层或过滤器。由于不存在巩固任意预先训练的模型组件的知识和技术，本提案旨在研究实现这一目标的基本科学和工程可能性。换句话说，我们的长期愿景是建立科学知识和技术诀窍，以实现一种新的网络建设范例，对组件进行选择，并在添加组件时保守。这开辟了几个研究途径，包括研究量化任务和网络相似性的方法，视觉应用中的眼睛凝视跟踪以量化网络的不同组成部分在分析图像中感兴趣的区域或“注意力”方面的重要性，以及加深我们对不同网络层中过滤器交互方式的理解。成功实现我们的目标将在几个领域带来实实在在的好处。一个例子是用于医疗保健和其他应用的EDGE AI领域，在这些领域，部署在传感器站点的网络的效率至关重要。另一个重要的结果是绿色人工智能，在这种情况下，减少在网络中运行样本所使用的能量要求在选择网络体系结构时采取更慎重的方法。拟议研究可能产生影响的另一个领域是需要密集特征集的应用，例如医疗保健中的放射组学应用。

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.