You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
The answer to this question is somewhat incorrect. In particular, the convolution operator only has the translational equivariance property. In this case, several factors that can constitute the translational invariance property of CNNs. We could argue that the translational invariance is due to the usage of pooling layers which downsamples the feature maps and thus makes the model less sensitive to small translations. Or, data augmentation helps the model learn to be more robust to positional displacements. Ultimately, CNNs are not translational invariant by design, see this paper.
The text was updated successfully, but these errors were encountered:
The answer to this question is somewhat incorrect. In particular, the convolution operator only has the translational equivariance property. In this case, several factors that can constitute the translational invariance property of CNNs. We could argue that the translational invariance is due to the usage of pooling layers which downsamples the feature maps and thus makes the model less sensitive to small translations. Or, data augmentation helps the model learn to be more robust to positional displacements. Ultimately, CNNs are not translational invariant by design, see this paper.
The text was updated successfully, but these errors were encountered: