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

海量的数字化图像和视频现在普遍可用，搜索引擎的出现进一步方便了它们的访问。这为应用机器学习技术来模拟人类的视觉感知创造了一个难得的机会。然而，这些数据往往不符合机器学习的核心假设，即训练图像和测试图像来自完全相同的分布或“域”。在实践中，训练分布和测试分布往往有所不同，甚至分布可能会随着时间的推移而漂移。例如，在Flickr上训练的“狗”探测器可以在可穿戴式相机的图像上进行测试，在不同的视角和照明条件下可以看到狗。因此，补偿这些变化的问题--领域适应问题--必须在理论和实践中得到解决，才能使算法有效。这个问题不仅仅是二阶效应，其解决方案并不构成性能的小幅提升。这个项目将开发一套用于感知自适应表征学习(PEAR)的核心理论和算法，当给定一个新的任务领域，以及以前对相关任务和领域的经验时，将提供一个学习体系结构，可能在新任务上实现最佳泛化。我们预计，PEARL将通过提供一个亟需的理论和计算框架，采取步骤统一领域适应理论和领域适应实践的子领域，对研究界产生重大影响。我们的理论和实践进步将通过允许在新的情况下和跨越空间和时间使用预先训练的感知模型(视觉和其他)来影响许多应用领域。例如，在移动技术和机器人技术方面，珀尔将帮助个人助理和机器人更好地适应个人用户和特定情境环境的感知界面。该项目的核心是三个主要的研究方向：1)通过发展广义差异距离最小化方法，为领域自适应提供理论支持；2)利用广义差异距离的理论保证，针对深度知觉表征学习、主动学习领域自适应和时间依赖自适应等关键自适应场景开发算法；3)提出多源自适应场景的理论并开发算法。除了我们的核心目标之外，我们计划在一个可伸缩的开源框架内实现我们的算法，并在大规模视觉数据集上评估我们的算法。

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.