Xception: Deep Learning with Depthwise Separable Convolutions

Deep convolutional neural networks have revolutionized computer vision, with architectures like VGG, ResNet, and Inception setting benchmarks. However, these models often involve complex structures and high parameter counts, limiting deployment on resource-constrained devices. The paper introduces Xception, a novel architecture built entirely from depthwise separable convolutions, inspired by the interpretation of Inception modules as a spectrum between regular and separable convolutions. By stacking these efficient operations with residual connections, Xception achieves superior accuracy on large-scale datasets such as ImageNet and JFT, with comparable parameter counts. The key insight is that decoupling cross-channel and spatial correlations allows for more efficient parameter use, leading to performance gains without increasing model size. Extensive experiments demonstrate that Xception outperforms Inception V3 by a margin of 0.8% in Top-1 accuracy on ImageNet and improves MAP@100 by 4.3% on JFT, highlighting its robustness and scalability. This work not only advances theoretical understanding but also offers practical benefits for deploying high-performance models on mobile and cloud platforms. The architecture’s simplicity, combined with its effectiveness, paves the way for future innovations in lightweight, scalable CNNs, emphasizing the importance of parameter efficiency and modular design. Future research will explore intermediate architectures, hardware acceleration, and broader applications beyond classification, aiming to make deep learning models more accessible and adaptable across diverse real-world scenarios.