Which of the following are true about Depthwise-separable convolutions? (Choose all that apply)

Which of the following are true about Depth wise-separable convolutions? (Choose all that apply)1 point

Question 9In Depthwise Separable Convolution you:

Which of the following can a user choose when designing a convolutional layer? (Choose all that are correct.)1 pointFilter numberFilter weightsFilter sizeFilter depthFilter stride

Which is false?Group of answer choicesOutput of a layer in the residual network is the normal output (i.e, what’s produced after applying a filter and an activation function) + the layers input.Output of a layer in the dense network is the normal output (i.e, what’s produced after applying a filter and an activation function) + the layers input.Output of layer 𝑙 in the dense network will be one of the inputs for layers 𝑖∈(𝑙+1,𝐿) where 𝐿 is the total number of layers.A problem of convolution network is that some features may get extracted earlier in the network, but could be useful later on. However, it is hard to keep track of previous non-modified outputs.

Which of the following statements is TRUE about Convolution Neural Networks (CNNs)?1 pointCNN can be applied ONLY on Image dataCNN can be applied on ANY 2D and 3D array of data.CNN can be applied ONLY on Text and Speech data.CNN can be applied ONLY on Image and Text data.All of the above