dujilo.blogg.se - Building backbone network definition

They are themselves divided into 2 main steps :ĭepthwise Convolution is a first step in which instead of applying convolution of size \(d \times d \times C\), we apply a convolution of size \(d \times d \times 1\). To overcome the cost of such operations, depthwise separable convolutions have been introduced. Therefore, for N Kernels (depth of the convolution) : Where K is the resulting dimension after convolution, which depends on the padding applied (e.g padding “same” would mean A = K). Here is the convolution process illustrated : We apply on it a convolution filter of size d*d, say 3*3. It also has a certain dimension A, say 100 * 100.

The input image has a certain number of channels C, say 3 for a color image. Convolution is a really expensive operation. The limits of convolutionsįirst of all, let’s take a look at convolutions.

Shortcuts between Convolution blocks as in ResNetĭepthwise Separable Convolutions are alternatives to classical convolutions that are supposed to be much more efficient in terms of computation time.

XCeption is an efficient architecture that relies on two main points : Xception architecture has overperformed VGG-16, ResNet and Inception V3 in most classical classification challenges. Note that all Convolution and SeparableConvolution layers are followed by batch normalization. The data first goes through the entry flow, then through the middle flow which is repeated eight times, and finally through the exit flow. What is an XCeption network? What does it look like? This observation leads them to propose a novel deep convolutional neural network architecture inspired by Inception, where Inception modules have been replaced with depthwise separable convolutions. In this light, a depthwise separable convolution can be understood as an Inception module with a maximally large number of towers.

Google presented 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). Xception is a deep convolutional neural network architecture that involves Depthwise Separable Convolutions.