Machine Learning for Perception

感知机器学习

• 批准号: 9185-2012
• 负责人: Hinton, Geoffrey
• 金额: $ 8.89万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2013
• 资助国家: 加拿大
• 起止时间: 2013-01-01 至 2014-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=521234
• 关键词: Machine; Learning; Perception

Computers are still much worse than people at recognizing objects in images or words in sound-waves. New methods for training artificial neural networks have improved their abilities to learn multiple layers of features and this has led to significant improvements in recognition, but artificial neural networks still lag far behind the real thing. The representational power of neural networks can be greatly improved by grouping neurons into "capsules" that each perform a lot of internal computation and output a small set of real numbers that are bound together as properties of the same entity. By using pairs of images related by a known transformation it is possible to allow each capsule to decide for itself what kind of visual or acoustic entity it should represent but to force each capsule to output the same kinds of coordinates as are used to handle viewpoint effects in computer graphics. This allows spatial relationships to be modeled as linear operations which makes it easy to recognize objects or words by using the relationships between their parts. Low level capsules learn to represent simple entities in small regions of the image. Higher-level capsules learn to represent more complex entities and they operate over larger regions of the image but they retain precise information about the position, orientation and scale of the visual entity they represent. This allows spatial relationships between parts to be used to recognize entities at many different levels in the whole-part hierarchy.

计算机仍然比识别声波中图像或单词中的对象的人要差得多。 培训人工神经网络的新方法提高了其学习多层特征的能力，这导致了识别的显着改善，但是人工神经网络仍然远远落后于真实事物。通过将神经元分组为“胶囊”，可以极大地改善神经网络的代表力，即每个神经元执行大量内部计算并输出一小部分实际数字，这些数字被绑定在一起作为同一实体的属性。通过使用已知转换相关的一对图像，可以允许每个胶囊自行决定它应该代表的视觉或声学实体，但强迫每个胶囊输出与处理计算机图形中观点效应相同类型的坐标。 这允许将空间关系建模为线性操作，从而使使用其部分之间的关​​系易于识别对象或单词。低级胶囊学会代表图像小区域中的简单实体。 高级胶囊学会代表更复杂的实体，并且在图像的较大区域上运行，但它们保留了有关其代表的视觉实体的位置，方向和规模的精确信息。这允许零件之间的空间关系用于识别整个部分层次结构中许多不同级别的实体。