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.

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.

What is the advantage of using attention mechanisms over traditional pooling techniques?Question 4Answera.Pooling techniques are less prone to overfitting.b. Attention mechanisms are more computationally efficient.c. Pooling techniques provide better interpretability.d. Attention mechanisms allow for more flexible and adaptive feature selection

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

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.