Xception

Xception is a deep convolutional neural network architecture designed for image classification. Introduced by François Chollet in 2017, the name stands for "Extreme Inception," signaling its relation to the Inception family while adopting a different factorization approach. The core idea is to replace standard convolutions with depthwise separable convolutions to decouple cross-channel correlations from spatial correlations, aiming to improve efficiency without sacrificing accuracy.

The architecture is built from a linear stack of depthwise separable convolutional layers organized into three main flows: entry flow, middle flow, and exit flow. It comprises 36 convolutional layers that employ depthwise separable convolutions throughout, with residual connections to ease the training of a deep network. The network typically processes images with a 299 by 299 pixel input size, concluding with global average pooling and a dense softmax classifier for the final predictions.

In practice, Xception emphasizes parameter efficiency by factorizing convolutions, similar in spirit to depthwise separable convolutions used in other models such as MobileNets. It achieved strong performance on ImageNet and has been widely used for transfer learning, often serving as a feature extractor or fine-tuning base in various image recognition tasks.

Availability and usage: Xception is implemented in major deep learning frameworks and distributions, with pre-trained ImageNet weights commonly provided (for example, in Keras Applications). While it remains a reference point for depthwise separable convolution design, newer architectures may offer different trade-offs in accuracy and computational requirements.