AitF: FULL: Collaborative Research: PEARL: Perceptual Adaptive Representation Learning in the Wild

AitF：FULL：协作研究：PEARL：野外感知自适应表示学习

• 批准号: 1536003
• 负责人: Trevor Darrell
• 金额: $ 20万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1536003&HistoricalAwards=false
• 关键词: AitF; FULL; Collaborative; Research; PEARL

Vast amounts of digitized images and videos are now commonly available, and the advent of search engines has further facilitated their access. This has created an exceptional opportunity for the application of machine learning techniques to model human visual perception. However, the data often does not conform to the core assumption of machine learning that training and test images are drawn from exactly the same distribution, or "domain." In practice, the training and test distributions are often somewhat dissimilar, and distributions may even drift with time. For example, a "dog" detector trained on Flickr may be tested on images from a wearable camera, where dogs are seen in different viewpoints and lighting conditions. The problem of compensating for these changes--the domain adaptation problem--must therefore be addressed both in theory and in practice for algorithms to be effective. This problem is not just a second-order effect and its solution does not constitute a small increase in performance. Ignoring it can lead to dramatically poor results for algorithms "in the field."This project will develop a core suite of theory and algorithms for PErceptual Adaptive Representation Learning (PEARL), which, when given a new task domain, and previous experience with related tasks and domains, will provide a learning architecture likely to achieve optimal generalization on the new task. We expect PEARL to have a significant impact on the research community by providing a much-needed theoretical and computational framework that takes steps toward unifying the subfields of domain adaptation theory and domain adaptation practice. Our theoretical and practical advancements will impact many application areas by allowing the use of pre-trained perceptual models (visual and otherwise) in new situations and across space and time. For example, in mobile technology and robotics, PEARL will help personal assistants and robots better adapt their perceptual interfaces to individual users and particular situated environments. At the core of this project are three main research thrusts: 1) making theoretical advances for domain adaptation by developing generalized discrepancy distance minimization; 2) using the theoretical guarantees of generalized discrepancy distance to develop algorithms for key adaptation scenarios of deep perceptual representation learning, domain adaptation with active learning, and time-dependent adaptation; 3) advancing the theory and developing algorithms for the multiple-source adaptation scenario. In addition to our core aims, we plan to implement our algorithms within a scalable open-source framework, and evaluate our algorithms on large-scale visual data sets.

现在通常可以使用大量数字化的图像和视频，搜索引擎的出现进一步促进了它们的访问。这为应用机器学习技术来建模人类视觉感知创造了出色的机会。但是，数据通常不符合机器学习的核心假设，即训练和测试图像是从完全相同的分布或“域”中得出的。在实践中，培训和测试分布通常有些不同，并且分布甚至可能随着时间的流逝而漂移。例如，可以在可穿戴相机的图像上测试接受过Flickr的“狗”检测器，在该图像中，在不同的观点和照明条件下可以看到狗。弥补这些变化的问题 - 域适应问题 - 因此，在理论和实践中都可以解决算法有效的问题。这个问题不仅是二阶效应，其解决方案并不构成少量的性能。 忽略它可能会导致算法的巨大结果。任务和域将提供一种可能在新任务上实现最佳概括的学习体系结构。我们希望珍珠通过提供急需的理论和计算框架对研究界产生重大影响，该框架采取了统一域名适应理论和领域适应实践的子场的步骤。我们的理论和实践进步将通过在新情况以及跨时时间和时间和时间和时间和时间和时间上使用预训练的感知模型（视觉和其他方式）来影响许多应用领域。例如，在移动技术和机器人技术中，Pearl将帮助个人助理和机器人更好地调整其感知界面，以适应个人用户和特定位置环境。 该项目的核心是三个主要的研究作用：1）通过开发一般的差异距离最小化来实现领域适应的理论进步； 2）使用广义差异距离的理论保证来开发算法，以进行深度感知表示学习的关键适应方案，主动学习的域适应和时间依赖性适应； 3）推进理论并为多种源适应方案开发算法。除了我们的核心目标外，我们还计划在可扩展的开源框架内实现算法，并在大规模的视觉数据集上评估我们的算法。