How does the architecture of a CNN ensure translational invariance?
- CNNs use weight sharing in convolutional layers, making features invariant to translation
- CNNs utilize pooling layers to reduce feature maps size
- CNNs randomly initialize weights to break translational invariance
- CNNs use a large number of layers for translation invariance
CNNs ensure translational invariance by sharing weights in convolutional layers, allowing learned features to detect patterns regardless of their location in the image. This is a key property of CNNs.
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