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

大量的数字化图像和视频现在随处可见，搜索引擎的出现进一步促进了对它们的访问。这为应用机器学习技术来模拟人类视觉感知创造了一个绝佳的机会。然而，这些数据往往不符合机器学习的核心假设，即训练和测试图像是从完全相同的分布或“域”中提取的。在实践中，训练分布和测试分布常常有些不同，而且分布甚至可能随着时间的推移而漂移。例如，在Flickr上训练的“狗”检测器可能会对来自可穿戴相机的图像进行测试，其中狗在不同的视角和光照条件下被看到。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，以使算法有效。这个问题不仅仅是二阶效应，它的解决方案也不会带来性能的小幅提高。忽略它可能会导致“在该领域”的算法得到非常糟糕的结果。该项目将为感知自适应表示学习（PEARL）开发一套核心理论和算法，当给定一个新的任务领域，以及之前相关任务和领域的经验时，将提供一个可能在新任务上实现最佳泛化的学习架构。我们期望PEARL通过提供一个急需的理论和计算框架来统一领域适应理论和领域适应实践的子领域，从而对研究界产生重大影响。我们的理论和实践进展将影响许多应用领域，允许在新情况下和跨空间和时间使用预训练的感知模型（视觉和其他）。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适应个人用户和特定环境的感知界面。本项目的核心研究重点有三个方面：1)通过发展广义差异距离最小化来推进领域自适应的理论研究；2)利用广义差异距离的理论保证，开发深度感知表征学习、主动学习的领域适应和时间依赖适应等关键适应场景的算法；3)提出多源自适应场景的理论和算法。除了我们的核心目标，我们计划在一个可扩展的开源框架内实现我们的算法，并在大规模的可视化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.