- Xception: Deep Learning with Depthwise Separable Convolutions
We show that this architecture, dubbed Xception, slightly outperforms Inception V3 on the ImageNet dataset (which Inception V3 was designed for), and significantly outperforms Inception V3 on a larger image classification dataset comprising 350 million images and 17,000 classes
- Xception详解 - 知乎
深度可分离卷积 （Depthwise Separable Convolution）率先是由 Laurent Sifre在其博士论文《Rigid-Motion Scattering For Image Classification》 [2]中提出。 经典的 MobileNet [3]系列算法便是采用深度可分离卷积作为其核心结构。 这篇文章主要从 Inception [4]的角度出发，探讨了Inception和深度可分离卷积的关系，从一个全新的角度解释了深度可分离卷积。 再结合stoa的 残差网络 [5]，一个新的架构Xception应运而生。 Xception取义自Extreme Inception，即Xception是一种极端的Inception，下面我们来看看它是怎样的一种极端法。 1
- Xception. Xception, an abbreviation for “Extreme… | by . . . - Medium
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
- Xception - Keras
keras applications Xception( include_top=True, weights="imagenet", input_tensor=None, input_shape=None, pooling=None, classes=1000, classifier_activation="softmax", name="xception", )
- XCeption Model and Depthwise Separable Convolutions
Xception is a deep convolutional neural network architecture that involves Depthwise Separable Convolutions It was developed by Google researchers
- GitHub - molyswu xception: Xception is a novel deep convolutional . . .
Xception is a novel deep convolutional neural network architecture, where Inception modules have been replaced with depthwise separable convolutions With a similar parameter count, Xception significantly outperforms Inception V3 on a larger image classification dataset called JFT
- Xception Model: Analyzing Depthwise Separable Convolutions
Xception, short for Extreme Inception, is a Deep Learning model developed by Francois Chollet at Google, continuing the popularity of Inception architecture, and further perfecting it
- Xception: Deep Learning with Depth-wise Separable Convolutions
Xception is a deep convolutional neural network architecture that involves Depthwise Separable Convolutions This network was introduced Francois Chollet who works at Google, Inc (Fun-Fact: He is the creator of keras) Xception is also known as “extreme” version of an Inception module
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