Big Data Processing and Analytics - Mining Noisy Visual Data and Learning Transferrable Predictive Models

最近出现了“大数据”一词，用以描述涉及捕获、管理、处理、分析和使用大量数据的各种技术和问题。我们捕获、存储和处理数据的能力的提高，已经到了大数据的影响现在可以在国家报纸的头版看到的地步。申请者在处理各种典型的大数据问题方面拥有丰富的经验，包括之前获得的一项关于“大规模数据挖掘”的发现资助。研究将侧重于开发广泛适用的视觉数据处理和分析技术，通过研究涉及可能产生高影响的视觉数据的各种问题。关键领域将包括医学图像分析、对象和活动识别，重点是视频索引、下一代计算机动画、智能交通和机器人技术的应用。**研究将集中在以下常见需求、问题、挑战和研究问题上，这些问题是通过申请人与以前的大数据研究项目的第一手经验确定的，即：1)需要能够处理大数据集的加速技术，包括预处理、特征提取、数据建模、优化和分析技术。2)需要原则性的理论和技术的实施，以优化相关的性能测量，同时也考虑到不同的预处理、模型复杂性、资源限制以及标记数据与未标记数据的数量。3)与如何最有效地利用潜在的大量未标记数据以及弱或部分和/或噪声标记的数据相关的挑战。4)与数据收集和减少人工标记工作相关的问题。5)如何更有效地将使用一个领域中的数据获得的学习模型或表示转移到另一个领域的未决问题。**为了实现这些目标，我们将使用标准评估数据集进行实验，并进一步开发、管理和创建一些我们自己的数据集，涵盖人脸、情感、人类活动、场景类型、对象和医学图像等主题。**为了获得大量弱或有噪声标记的数据，并对有噪声标记的学习方法进行实验，我们将进一步开发已由面向盲人的描述性视频服务标注的大型视频数据集。我们还将收集我们自己的以智能交通、机器人和计算机动画为主题的3D和4D对象和活动识别数据集。**最近的研究将大型数据集与深度神经网络学习技术的高度加速优化相结合，在各种竞争问题上取得了令人印象深刻的结果。在这里，我们将探索和比较其他新型深层架构和其他技术可以从大数据和算法加速的组合中受益的方式。然后将使用加速算法来开发完全优化管道的技术，包括前处理步骤和超参数。**我们还希望在应用于在相关但不同环境中收集的测试数据时增强模型和表示的可转移性。我们假设，如果使用代表域转移的底层类型的验证集，则完整的流水线优化可能会导致更多可转移的方法。对于视觉识别，我们还假设，明确考虑到我们世界的4D本质的技术可能会产生更好的可转移性。**该项目将在大数据需求高的领域培养高素质的人才。

The term `Big Data' has recently emerged to characterize a wide variety of techniques and problems that involve the capture, management, processing, analysis and use of large quantities of data. The increase in our ability to capture, store and process data has reached a point where the impact of Big Data is now seen on the front page of national newspapers. The applicant has extensive experience across a variety of prototypical Big Data problems, including a previous discovery grant on `large scale data mining'. Research will focus on developing broadly applicable techniques for visual data processing and analysis through looking at a variety of problems involving visual data with the potential for high impact. Key areas will consist of medical image analysis, object and activity recognition focusing on applications to video indexing, next generation computer animation, intelligent transportation and robotics.**Research will focus upon the following common needs, problems, challenges and research questions identified through the applicant's first hand experience with previous Big Data research projects, namely: 1) The need for acceleration techniques capable of processing large data sets including pre-processing, feature extraction, data modeling, optimization and analysis techniques. 2) The need for principled theory and implementations of techniques that optimize over relevant measures of performance while also accounting for different pre-processing, model complexity, resource constraints and the amount of labelled vs. unlabeled data. 3) The challenges associated with how to most effectively exploit potentially enormous quantities of unlabeled data, as well as weakly or partially and/or noisily labelled data. 4) The problems associated with data collection and reducing human labelling effort. 5) The open questions of how to more effectively transfer learned models or representations obtained using data in one domain to another domain. **To achieve these goals we will perform experiments using standard evaluation data sets as well as further develop, curate and create a number of our own data sets covering the themes of faces, emotions, human activities, scene types, objects and medical imagery. **In particular, to obtain large quantities of weakly or noisily labelled data and perform experiments on methods for learning with noisy labels we will further develop a large dataset of video that has been annotated by Descriptive Video Services for the blind. We will also collect our own 3D and 4D object and activity recognition data sets centred on the themes of intelligent transportation, robotics and computer animation.**Recent research combining large data sets with highly accelerated optimization of deep neural network learning techniques has yielded impressive results on a wide variety of competitive problems. Here we will explore and compare the ways in which other novel deep architectures and other techniques can benefit from the combination of big data and algorithm acceleration. Accelerated algorithms will then be used to develop techniques for the complete optimization of pipelines including both pre-processing steps and hyper-parameters.**We also wish to enhance the transferability of models and representation when applied to test data that has been collected in related but different settings. We hypothesize that complete pipeline optimization may lead to more transferrable methods if validation sets representative of the underlying types of domain transfer are used. For visual recognition, we also hypothesize that techniques explicitly accounting for the 4D nature of our world may yield improved transferability. **The project will result in the training of highly qualified personnel in the high demand area of Big Data.