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: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=568652
• 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. Another way to significantly improve the performance of artificial neural networks is to use a much better optimizer that was recently developed at the University of Toronto for learning the weights of a recurrent neural network. The new optimizer makes it possible to train neural networks to use regularities at many different time scales to model very complicated sequential data such as speech. We plan to combine all of the main stages of an automatic speech recognition system into a single network that can be be trained to convert sound waves into sequences of recognized words.

在识别图像中的物体或声波中的文字方面，计算机仍然比人差得多。 训练人工神经网络的新方法提高了它们学习多层特征的能力，这导致了识别方面的显著改进，但人工神经网络仍然远远落后于真实的事物。通过将神经元分组为“胶囊”，每个胶囊执行大量的内部计算并输出一小组真实的数字，这些数字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。通过使用由已知变换相关的图像对，可以允许每个胶囊自己决定它应该表示哪种视觉或听觉实体，但是迫使每个胶囊输出与用于处理计算机图形中的视点效果的坐标相同的坐标。 这允许空间关系被建模为线性操作，这使得通过使用其部分之间的关系来识别对象或单词变得容易。低级胶囊学习在图像的小区域中表示简单的实体。 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操作，但它们保留了关于它们所表示的视觉实体的位置，方向和尺度的精确信息。这允许使用部件之间的空间关系来识别整体-部件层次结构中许多不同级别的实体。 另一种显著提高人工神经网络性能的方法是使用多伦多大学最近开发的一种更好的优化器，用于学习递归神经网络的权重。新的优化器可以训练神经网络在许多不同的时间尺度上使用递归来模拟非常复杂的序列数据，如语音。我们计划将自动语音识别系统的所有主要阶段联合收割机组合成一个单一的网络，可以训练该网络将声波转换为已识别单词的序列。