The ‘pooling layer’ is used for dimension reduction; therefore,computational time and model complexity is reduced withoutaffecting the most important features. As compared to averagepooling, max-pooling is used so that model complexity isreduced. The maximum value of the image pixel is consideredrather than the average of all pixels, in average pooling.

In a convolutional neural network (CNN), what is the primary role of the pooling layer? Question 2 options: Reducing the number of parameters in the network. Introducing non-linearities into the network. Performing element-wise multiplication with the filter weights. Down-sampling the feature maps to reduce spatial dimensions.

Which of the following helps to reduce the number of parameters of an input image and still preserves the important features?1 pointPoolingFlatteningLayerReceptive field

Which layer in a CNN is responsible for reducing the spatial dimensions of the input data?a.Convolutional layerb.Activation layerc.Fully connected layerd.Pooling layer

What statements are true?Group of answer choicesBy the end of our network, if we have multiple convolution and pooling layers, our final image is smaller than the original input image and we can classify the entire image, not just a region.Two common pooling methods are max pooling and mean pooling.If you use Photoshop to switch the location of the eyes and the mouth of a cat, a convolutional net with pooling layers would likely still "recognize" the image as a cat.Pooling introduces translation invariance by conveying what patterns are apparent in the image, without conveying where they are exactly.

In CNN if the prominent features are to be extracted, the preferred pooling layer will be*1 pointMinMaxMedianAverage