pooling和旋转不变性

Pooling for Invariance

If one chooses the pooling regions to be contiguous areas in the image and only pools features generated from the same (replicated) hidden units. Then, these pooling units will then be translation invariant. This means that the same (pooled) feature will be active even when the image undergoes (small) translations. Translation-invariant features are often desirable; in many tasks (e.g., object detection, audio recognition), the label of the example (image) is the same even when the image is translated. For example, if you were to take an MNIST digit and translate it left or right, you would want your classifier to still accurately classify it as the same digit regardless of its final position.


原文地址:http://wiki.suanfazu.com/w/Pooling

          http://www.deeplearning.net/tutorial/lenet.html#lenet

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