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Learning to Be (In)variant: Combining Prior Knowledge and Experience to Infer Orientation Invariance in Object Recognition

机译:学会变(不变):结合先验知识和经验以推断物体识别的方向不变性

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摘要

How does the visual system recognize images of a novel object after a single observation despite possible variations in the viewpoint of that object relative to the observer? One possibility is comparing the image with a prototype for invariance over a relevant transformation set (e.g., translations and dilations). However, invariance over rotations (i.e., orientation invariance) has proven difficult to analyze, because it applies to some objects but not others. We propose that the invariant transformations of an object are learned by incorporating prior expectations with real-world evidence. We test this proposal by developing an ideal learner model for learning invariance that predicts better learning of orientation dependence when prior expectations about orientation are weak. This prediction was supported in two behavioral experiments, where participants learned the orientation dependence of novel images using feedback from solving arithmetic problems.
机译:尽管对象相对于观察者的视线可能发生变化,但视觉系统在一次观察后如何识别新对象的图像?一种可能性是将图像与原型相关的变换集(例如,平移和膨胀)进行比较。然而,事实证明,旋转的不变性(即方向不变性)难以分析,因为它适用于某些对象,但不适用于其他对象。我们建议通过将先前的期望与现实世界的证据相结合来学习对象的不变变换。我们通过为学习不变性开发理想的学习者模型来测试该建议,该模型将在先前对取向的期望较弱时预测更好地学习取向依赖性。这项预测得到了两个行为实验的支持,其中参与者使用解决算术问题的反馈来学习新颖图像的方向依赖性。

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