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

机器学习是一门设计计算系统的科学，它可以像人类一样从数据中学习。然而，尽管许多机器学习方法依赖于人类为用于学习的训练实例提供标签，但人类提供的标签只代表了可以从人类收集的信息的一小部分。该项目汇集了一个拥有计算机科学、神经科学和心理学专业知识的多学科团队，以开创机器学习的新范式，寻求通过纳入关于人类行为的新型信息来更好地模拟人类的表现。具体地说，该项目将寻求定量描述人类表现的心理物理学和心理计量学学科与机器学习结合起来，开发从人类成功和失败中学习的系统，以生成表现更好、对训练集以外的数据更好的人工系统。在将这些想法应用到困难、杂乱的真实世界图像中的人脸检测问题上，项目团队已经展示了初步的概念证明。在项目期间，该团队将极大地扩展这些想法，开发新的应用程序(包括人脸和对象识别任务)、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的方法(如功能磁共振脑扫描)，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，在我们这个由数据驱动的现代世界中，机器学习的影响越来越重要和广泛。此外，预计从这项工作中得出的机器学习的理论成果将反馈到心理学中，使人工系统能够快速筛选关于大脑如何工作的候选假设，然后可以使用先进的众包平台来量化人类行为，在人类身上进行测试。

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.