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

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.

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

11. Which of the following is a correct statement about a CNN (Convolution Neural Network) structure?以下哪個是關於卷積神經網路架構的正確說法？Number classification and identification is the main function of convolution layers and pooling layers, while fully connected layers are mainly used for image feature extraction. 卷積層與池化層的功能主要是數字分類辨識，而全連結層則主要用於擷取影像的特徵。There have to be more convolution layers than pooling layers. 卷積層的數量需要比池化層多。There have to be more fully connected layers than convolution and pooling layers. 全連接層的數量需要比卷積層與池化層多。Convolution layers are used to extract features from images, while fully connected layers are mainly used to make a classification decision. 卷積層主要是用來擷取影像的特徵，而全連結層則主要用於決定如何分類。