Max pooling layers are:

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Question: Max pooling layers are:

Option 1: No answer text provided. This is not a substantive option and cannot describe a property of max pooling.

Option 2: Invariant to large translations. In practice, max pooling provides some degree of translational robustness, but it is not perfectly invariant to large translations because large shifts can move features out of the pooled regions or change which values are selected, especially with fixed kernel size and stride. So this overstates the invariance.

Option 3: Equivariant to small translations. Equivariance would mean that translating the input results in an equivalent translation of the output. Max pooling does not preserve spatial structure in a way that yields a simple translated output; the operation reduces resolution and can change which maxima are selected. Therefore, this statement mischaracterizes max pooling.

Option 4: Invariant to small translations. This is the commonly stated property: because pooling aggregates information within a local window (taking the maximum), small shifts in the input that occur within that window often produce the same pooled value, creating a degree of translation invariance at those scales.

Option 5: Invariant to all translations. This is incorrect because, as noted, large translations or shifts that move features across pooling windows can change the output, so invariance to all translations is not achieved.

Summary: The most accurate description among the options is that max pooling layers are invariant to small translations, since small shifts often do not change which values are maximal within each pooling region, while larger shifts can alter the results.

CS-7643-O01, OAN, OSZ Quiz #2: Optimization of Deep Neural Networks (Lesson 3) and Convolution and Pooling Layers (Lesson 5)

Max pooling layers are:

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