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

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.

“Parameter sharing" - A benefit of using convolutional networks. Which of the following statements about parameter sharing in CNN are true?Question 1Answera.  It reduces the total number of parameters, thus reducing overfitting.b.  More than one option is correctc.   It allows parameters learned for one task to be shared even for a different task (transfer learning).d.  It allows a feature detector to be used in multiple locations throughout the whole input image/input volume.

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.

Question 7Which of the following are benefits of pooling? (Choose all that are correct.)1 pointDecreases bias.Reduces computational complexity.Encourages translational invariance.Combats overfitting.Vectorizes the data.

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