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depth
Go back to the [[AI Glossary]]
The number of layers (including any embedding layers) in a neural network that learn weights. For example, a neural network with 5 hidden layers and 1 output layer has a depth of 6.
depthwise separable convolutional neural network (sepCNN)
Go back to the [[AI Glossary]]
A convolutional neural network architecture based on Inception, but where Inception modules are replaced with depthwise separable convolutions. Also known as Xception.
A depthwise separable convolution (also abbreviated as separable convolution) factors a standard 3-D convolution into two separate convolution operations that are more computationally efficient: first, a depthwise convolution, with a depth of 1 (n ✕ n ✕ 1), and then second, a pointwise convolution, with length and width of 1 (1 ✕ 1 ✕ n).
To learn more, see Xception: Deep Learning with Depthwise Separable Convolutions.
- public document at doc.anagora.org/depth|depth
- video call at meet.jit.si/depth|depth