Expressive data augmentation in deep learning

深度学习是机器学习的一个子领域，机器学习是一种人工智能，其目标是自动学习如何使用数据解决问题。例如，深度学习中的一个典型任务被称为“图像分类”，它包括学习如何在给定一个标有相应类别的图像数据集时将图像分类为不同类别（例如“猫”，“狗”，“人”）。对于人类来说，这项任务很容易，但由于计算机只能“看到”一堆1和0的图像，因此很难将如何解决问题编码为算法，即计算机可以遵循的一组机械指令。近年来，深度学习使设计此类算法很困难的新应用成为可能，在涉及图像、音频和语言等各种任务方面取得了进展。深度学习的一个关键限制是，它通常需要大量的数据才能正常工作。例如，在图像分类中，需要几千到几百万个标记的图像来获得合理的性能，这对于大多数应用来说是一个过高的成本。为了帮助补偿，通常会从现有数据中人工生成新数据，这一过程称为“数据增强”。一个基本的例子是随机地对图像的亮度进行轻微的改变，同时保持其标签-猫的图像仍然是猫的图像，即使亮度改变了一点点。数据扩充的作用是扩大用于学习算法的数据集的有效大小，而不需要昂贵的新数据收集。尽管其巨大的效用，但在使用数据增强时存在许多挑战，我的研究旨在解决这些挑战。例如，当将其应用于新问题时，需要定义其基本操作（例如随机亮度变化本身）并决定其精确强度，这可能是昂贵的。我的研究将通过学习操作来自动定义增强操作，这些操作可以改变图像的精确外观，同时保留所需的标签。然后，为了调整每个操作的强度，我的研究将调查在没有数据增强的情况下训练的算法的学习行为;如果算法输出相对于特定操作变化很大，那么很可能使用大量的数据作为数据增强将提高算法的鲁棒性。如果成功，我的研究将允许自动创建表达性数据增强策略，大大减少了在加拿大的研究和工业中解锁深度学习新应用所需的数据量。一个特别令人兴奋的应用是在医学上，因为大多数医疗任务可用的数据是有限的。理想情况下，改进和新型诊断的发展是可能的，推动加拿大的医学研究，并最终促进加拿大公众的健康。

Deep learning is a subfield of machine learning, a type of artificial intelligence, whose goal is to automatically learn how to solve problems using data. For example, a typical task in deep learning is called "image classification", and consists of learning how to categorize images into different categories (e.g. "cat", "dog", "human") when given a dataset of images labeled with their corresponding category. For a human, this task is easy, but since computers can only "see" an image as a bunch of ones and zeros, it is hard to encode how to solve the problem into an algorithm, a set of mechanical instructions that a computer can follow. In recent years, deep learning has enabled new applications where designing such algorithms is difficult, making advances on tasks involving images, audio, and language, among a variety of others. One key limitation of deep learning is that it typically requires a large amount of data in order to work well. In image classification, for example, several thousand to several million labeled images are required for reasonable performance, a prohibitive cost for most applications. To help compensate, it is common to artificially generate new data from existing data, a process known as "data augmentation". A basic example of this is to randomly make slight alterations to an image's brightness while maintaining its label - an image of a cat is still an image of a cat, even if the brightness is changed by a small amount. Data augmentation has the effect of expanding the effective size of the dataset used to learn algorithms without requiring the costly collection of new data. Despite its large utility, a number of challenges exist when using data augmentation, which my research intends to solve. When applying it to a new problem, for example, one needs to define its basic operations (e.g. the random brightness change itself) and decide on their precise strengths, which may be costly. My research will define augmentation operations automatically by learning operations that vary the precise appearance of images while preserving their desired labels. Then, to tune the strength of each operation, my research will investigate the learned behavior of algorithms trained without data augmentation; if algorithm output varies greatly with respect to a particular operation, then it is likely that using strong amounts of it as data augmentation will improve an algorithm's robustness to it. If successful, my research will allow for the automatic creation of expressive data augmentation policies, substantially reducing the amount of data required to unlock new applications of deep learning throughout both research and industry in Canada. One particularly exciting application of this is in medicine, since the data available for most medical tasks is limited. Ideally, the development of both improved and novel diagnostics may be possible, advancing Canadian medical research and eventually the health of the Canadian public as a whole.