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Chollet: Xception depthwise-separable convolutional architecture

Contribution grade

Computer vision architectures · Human-designed AI research method or resource

Reference score20,000Provisional editorial estimate of this exact contribution; its grade is assessed separately.

What changed

Reinterpreted Inception modules through depthwise-separable convolutions and proposed Xception, reporting image-classification gains at similar parameter count to Inception V3.

Scientific scope

Reinterpreted Inception modules through depthwise-separable convolutions and proposed Xception, reporting image-classification gains at similar parameter count to Inception V3. Scope limitation: First preprint was in 2016; year follows CVPR 2017 publication. The paper does not originate depthwise-separable convolution.

Evidence

The retained primary-source abstract, bibliographic record and stated review scope support this provisional assessment. No independent experimental replication by WRS is claimed. First preprint was in 2016; year follows CVPR 2017 publication. The paper does not originate depthwise-separable convolution.

Contributors

Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.

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