Towards Continual and Compositional Learning in the Visual World

人工智能和机器学习有望成为未来加拿大和全球经济的一个重要方面。然而，目前的机器学习系统仍然面临许多根本性的挑战。现有的机器学习，特别是深度学习系统通常应用于孤立的任务或狭窄的领域（如预定义对象的图像识别），使用来自孤立领域的大量数据。通常还假设在训练期间所有感兴趣的任务样本都是可用的。然而，一个更接近人类学习的现实场景是从不断变化的领域或任务的数据流中学习，可能在某些任务中只有很少的数据。这种持续学习者的目标是通过利用以前获得的知识来快速适应新的情况或任务，同时保护以前的知识不被抹去。关键的是，这必须在计算和内存限制下完成。例如，在从机器人系统到社交媒体平台的应用程序中，通常会出现这种情况。这个持续学习的问题最近已经被研究过，与静态系统相比，成功有限，并且与现实世界的应用程序脱节。特别是，最近的许多发展都集中在严重的内存限制上，而现实生活中的应用程序在计算预算上受到更大的限制，并且需要学习器观察更长的序列长度。关于持续学习的拟议研究的目标将是开发与现实世界用例相关的新数据集和评估协议，以及可以扩展到这些情况的方法和模型。尽管深度学习模型在许多领域都很重要，但它在计算、内存需求和在线学习能力方面都具有独特的属性。因此，这项工作将集中在深度学习模型及其应用上。在我们最初的工作中，我们的目标是在一组新的评估指标和数据集上评估各种现有的持续学习方法。然后，我们将提出和评估新的方法方向。我们将研究记忆和经验重放在解决持续学习中的作用，并同时设计低计算复杂度的方法，在接收新数据时利用先前的记忆。此外，我们将探索新的深度学习架构元素，特别是基于条件计算框架，它可以允许构建可重用组件，同时避免在不相关的系统模块中学习。将考虑多个应用领域，重点是计算机视觉和自然语言处理。一个长期的目标是将这项工作与涉及机器人代理在视觉丰富的环境中接受指令的计算机模拟问题联系起来。这种模型的一个用例是驱动家用机器人应用。

Artificial intelligence and machine learning hold promise to be a major aspect of the future Canadian and global economy. However the current class of machine learning systems still have many fundamental challenges. Existing machine learning and particularly deep learning systems are commonly applied to isolated tasks or narrow domains (such as image recognition of predefined objects), using large amounts of data from the isolated domain. It is also typically assumed that all the samples of the tasks of interest are available during training. However, a realistic scenario and one closer to human-like learning is learning from a stream of data from changing domains or tasks, potentially with little data in some tasks. The objective of such a continual learner is to quickly adapt to new situations or tasks by exploiting previously acquired knowledge, while protecting previous learning from being erased. Critically, this must be accomplished under computation and memory constraints. Such a scenario commonly arises for example in applications ranging from robotic systems to social media platforms. This continual learning problem has recently been studied, with limited success as compared to static systems and a disconnect from real-world applications. In particular many of the recent developments focus on drastic memory limitations, while real life applications are far more restricted in compute budget and have learners observing much longer sequence lengths. The goals of the proposed research on continual learning will be to develop novel datasets and evaluation protocols relevant for real-world use cases as well as methods and models which can scale to these situations. Although critical in many domains deep learning models have unique properties in terms of computation, memory requirements, and in terms of their online learning abilities. This work will thus focus on deep learning models and their applications. In our initial work we will aim to evaluate a wide variety of existing continual learning approaches on the set of new proposed evaluation metrics and datasets. Then we will propose and evaluate novel methodological directions. We will study the role of memory and experience replay for solving continual learning and devise concurrently low computational complexity ways to utilize prior memories while receiving new data. Additionally we will explore novel deep learning architectural elements in particular based on the conditional computation framework, which can allow the building of reusable components while avoiding learning in irrelevant system modules. Multiple application areas will be considered with a focus on computer vision and natural language processing. A long term aim will be to connect this work with problems involving computer simulations of robotic agents receiving instructions in a visually rich environment. One use case of such models being to drive household robotics applications.