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The 36 convolutional layers are structured into. 14 modules, all of which have linear residual connections around them, except for the first and last modules. In short, the Xception architecture is a linear stack of depthwise separable convolution layers with residual con-nections.
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Oct 6, 2016 · Xception is a convolutional neural network architecture that relies solely on depthwise separable convolution layers. Source: Xception: Deep Learning With Depthwise Separable Convolutions. Read Paper See Code.
Dec 28, 2023 · X ception, an abbreviation for “ Extreme Inception ,” represents a milestone in convolutional neural network (CNN) design. Conceived by François Chollet, the creator of the Keras deep learning...
- The Limits of Convolutions
- The Depthwise Convolution
- Pointwise Convolution
- Implementation of The Xception
First of all, let’s take a look at convolutions. Convolution is a really expensive operation. Let’s illustrate this : The input image has a certain number of channels C, say 3 for a color image. It also has a certain dimension A, say 100 * 100. We apply on it a convolution filter of size d*d, say 3*3. Here is the convolution process illustrated : N...
Depthwise Convolution is a first step in which instead of applying convolution of size d×d×Cd×d×C, we apply a convolution of size d×d×1d×d×1. In other words, we don’t make the convolution computation over all the channels, but only 1 by 1. Here is an illustration of the Depthwise convolution process : This creates a first volume that has size K×K×C...
Pointwise convolution operates a classical convolution, with size 1×1×N1×1×N over the K×K×CK×K×C volume. This allows creating a volume of shape K×K×NK×K×N, as previously. Here is an illustration of the Pointwise Convolution : Alright, this whole thing looks fancy, but did we reduce the number of operations? Yes we did, by a factor proportional to 1...
XCeption offers an architecture that is made of Depthwise Separable Convolution blocks + Maxpooling, all linked with shortcuts as in ResNet implementations. The specificity of XCeption is that the Depthwise Convolution is not followed by a Pointwise Convolution, but the order is reversed, as in this example :
Xception function. keras.applications.Xception( include_top=True, weights="imagenet", input_tensor=None, input_shape=None, pooling=None, classes=1000, classifier_activation="softmax", ) Instantiates the Xception architecture. Reference. Xception: Deep Learning with Depthwise Separable Convolutions (CVPR 2017)
Oct 7, 2016 · We present an interpretation of Inception modules in convolutional neural networks as being an intermediate step in-between regular convolution and the depthwise separable convolution operation (a depthwise convolution followed by a pointwise convolution).
Oct 21, 2020 · As we see in figure 1, the Xception module has 3 main parts. The Entry flow, the Middle flow (which is repeated 8 times), and the Exit flow. Figure 2. Entry flow of the Xception architecture (Source: Image from the original paper) The entry flow has two blocks of convolutional layer followed by a ReLU activation.
