RI: Medium: Deep Annotation: Measuring Human Vision to Improve Machine Vision

RI：中：深度注释：测量人类视觉以改善机器视觉

• 批准号: 1409097
• 负责人: David Cox
• 金额: $ 67.41万
• 依托单位: Harvard University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1409097&HistoricalAwards=false
• 关键词: RI; Medium; Deep; Annotation; Measuring

Machine learning is the science of designing computational systems that can learn from data, much as humans do. However, while many machine learning approaches rely on humans to provide labels for training examples that are used for learning, human-provided labels represent just a tiny fraction of the information that can be gleaned from humans. This project brings together a multidisciplinary team with expertise spanning computer science, neuroscience and psychology to pioneer a new paradigm in machine learning that seeks to better mimic human performance by incorporating new kinds of information about human behavior. Specifically, this project brings the disciplines of psychophysics and psychometrics, which seek to quantitatively describe human performance -- patterns of errors, reaction times, and variations across populations of humans -- together with machine learning to develop systems that learn both from human successes and failures, to generate artificial systems that perform better and generalize better to data outside of their training sets. The project team has already shown initial proof of concept in applying these ideas to the problem of face detection in difficult, cluttered real-world images. During the project period, the team will greatly expand these ideas, developing new applications (including face and object recognition tasks), a broader range of machine learning settings (including regression and feature selection), and methods for incorporating new kinds of data (such as fMRI brain scans) for guiding machine learning algorithms. This research represents a new direction in machine learning research, which increasingly has important and broad impact in our modern, data-driven world. In addition, it is anticipated that the theoretical gains in machine learning derived from this work will feed back into psychology, enabling rapid screening of candidate hypotheses about how the brain works by artificial systems which can then be tested on humans using an advanced crowdsourcing platform for quantifying human behavior.

机器学习是设计计算系统的科学，可以像人类一样从数据中学习。 然而，虽然许多机器学习方法依赖于人类为用于学习的训练示例提供标签，但人类提供的标签仅代表可以从人类收集的信息的一小部分。 该项目汇集了一支拥有计算机科学、神经科学和心理学专业知识的多学科团队，开创了机器学习的新范式，旨在通过整合有关人类行为的新型信息来更好地模仿人类表现。 具体来说，这个项目带来了心理物理学和心理测量学的学科，这些学科试图定量描述人类的表现-错误模式，反应时间和人类群体之间的变化-与机器学习一起开发从人类成功和失败中学习的系统，以生成表现更好的人工系统，并更好地概括训练集之外的数据。 项目团队已经展示了将这些想法应用于困难，混乱的现实世界图像中的人脸检测问题的初步概念证明。 在项目期间，该团队将大大扩展这些想法，开发新的应用程序（包括人脸和物体识别任务），更广泛的机器学习设置（包括回归和特征选择），以及整合新类型数据（如fMRI脑部扫描）的方法，以指导机器学习算法。 这项研究代表了机器学习研究的一个新方向，它在我们现代数据驱动的世界中越来越具有重要和广泛的影响。 此外，预计从这项工作中获得的机器学习的理论收益将反馈到心理学中，从而能够快速筛选人工系统关于大脑如何工作的候选假设，然后使用先进的众包平台对人类进行测试，以量化人类行为。