Why do we use convolutions for images rather than just FC layers?
Extract local features (conv layer outputs only depend on adjacent pixels of previous layer) Are equivariant to translation. Have lot less parameters, since they share kernel over the patches of whole input image.
Why are convolutions good for images?
The convolution layers are used to help the computer determine features that could be missed in simply flattening an image into its pixel values. First, a convolution uses a filter which is applied to the image in order to highlight certain features deemed important in the classification of the image.
What is the advantage of using Conv layers over FC dense layers?
The strength of convolutional layers over fully connected layers is precisely that they represent a narrower range of features than fully-connected layers. A neuron in a fully connected layer is connected to every neuron in the preceding layer, and so can change if any of the neurons from the preceding layer changes.
Is 1×1 convolution the same as dense layer?
A Linear layer and 1×1 convolutions are the same thing. It took me awhile to understand that there is no such thing as a “fully connected layer” – it’s simply a flattening of spatial dimensions into a 1D giant tensor.
Can a fully connected layer be replaced by a convolutional layer?
Yes, you can replace a fully connected layer in a convolutional neural network by convoplutional layers and can even get the exact same behavior or outputs. There are two ways to do this: 1) choosing a convolutional kernel that has the same size as the input feature map or 2) using 1×1 convolutions with multiple channels.
Can a convolutional layer detect dresses in an image?
In order to detect dresses in an image, we would need to first forward it throughout the convolutional layers. This will work as convolutional layers can adapt to larger input sizes. Assuming the convolutional and max pool layers reduce the input dimensions by a factor of 32, we would get an output of 32×16 units in the last convolutional layer.
Why are convolutions used in image processing tasks?
They have pictorial explanation of what it means for features to be equivariant (and also more details on the other points I mentioned). Convolution has proven to be useful in image processing for at least 40 years. That is why it is popular and also the reason to use convolutional layers in deep learning with images during the last 5 years or so.
How to convert fully connected layers into equivalents?
Here we are assuming that the input of the fully connected layer is flattened and also that the fully connected layer only receives a single feature map from the last convolutional layer.