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: 2012
• 资助国家: 加拿大
• 起止时间: 2012-01-01 至 2013-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=513355
• 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.

计算机在识别图像中的物体或声波中的文字方面仍然比人类差得多。 训练人工神经网络的新方法提高了其学习多层特征的能力，这导致了识别能力的显着提高，但人工神经网络仍然远远落后于真实的神经网络。通过将神经元分组为“胶囊”，每个“胶囊”执行大量内部计算并输出一小组实数，这些实数作为同一实体的属性绑定在一起，可以大大提高神经网络的表示能力。通过使用通过已知变换相关的图像对，可以允许每个胶囊自行决定它应该表示哪种视觉或听觉实体，但强制每个胶囊输出与用于处理计算机图形学中的视点效果相同类型的坐标。 这允许将空间关系建模为线性运算，从而可以通过使用对象或单词各部分之间的关​​系来轻松识别对象或单词。低级胶囊学习表示图像小区域中的简单实体。 更高级别的胶囊学习表示更复杂的实体，它们在图像的更大区域上运行，但它们保留有关它们所表示的视觉实体的位置、方向和比例的精确信息。这允许使用部件之间的空间关系来识别整个部件层次结构中许多不同级别的实体。