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Xception is a convolutional neural network (CNN) architecture widely used in deep learning for image classification. Proposed by Google in 2017, the name "Xception" stands for "Extreme Inception" and was developed based on the Inception architecture. The Xception architecture is particularly notable for its reliance on the principle of depthwise separable convolutions, which enables higher performance with fewer parameters. Xception Architecture Xception improves the information processing pipel

Xception is a convolutional neural network (CNN) architecture widely used in deep learning for image classification. Proposed by Google in 2017, its name stands for “Extreme Inception,” reflecting its inspiration from the Inception architecture. The Xception design is particularly notable for its reliance on the principle of depthwise separable convolutions, which enables higher performance with fewer parameters. Xception Architecture Xception improves the information processing pipeline by repl

Xception, derin öğrenmede görüntü sınıflandırma alanında yaygın olarak kullanılan bir konvolüsyonel sinir ağı (CNN) mimarisidir. Google tarafından 2017 yılında önerilen bu yapı, “Extreme Inception” (uç noktadaki Inception) anlamına gelir ve Inception mimarisinden esinlenerek geliştirilmiştir. Xception mimarisi, özellikle derin ayrık konvolüsyon (depthwise separable convolution) prensibine dayalı olmasıyla dikkat çeker. Bu yapı, daha düşük parametre sayısıyla daha yüksek performans elde etmeye ol