Xception
Xception, 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 library, Xception was introduced in 2017 as an evolution of the Inception architecture. In this blog post, we will delve into the architecture and approaches that distinguish Xception in the realm of deep learning.
Architecture:
1. Depthwise Separable Convolutions:
— The hallmark of Xception is its utilization of depthwise separable convolutions. Unlike traditional convolutions, which operate on both spatial and depth dimensions simultaneously, depthwise separable convolutions separate these operations. This significantly reduces the number of parameters and computational cost while maintaining representational power.
2. Separable Convolutions:
— Xception’s architecture is based on a series of separable convolutional blocks. Each block consists of a depthwise separable convolution followed by a linear transformation through a pointwise convolution. This modular design enhances the model’s ability to capture complex patterns with fewer parameters.
3. Entry Flow and Exit Flow:
— Xception is organized into an entry flow and an exit flow. The entry flow extracts features from the input, while the exit flow refines these features for final predictions. This hierarchical structure aids in learning hierarchical representations and facilitates the flow of information through the network.
4. Skip Connections:
— Inspired by the success of residual connections in ResNet, Xception incorporates skip connections within its architecture. These connections enable the direct flow of information across different layers, fostering the efficient training of deep networks.
Approaches:
1. Global Depthwise Separable Convolutions:
— Xception employs global depthwise separable convolutions in its final layers. This allows the network to capture global context and long-range dependencies, enhancing its understanding of complex relationships within the data.
2. Data Augmentation:
— Similar to other successful architectures, Xception benefits from data augmentation during training. This involves applying random transformations to the input data, augmenting the dataset and improving the model’s generalization to diverse scenarios.
3. Batch Normalization:
— Batch normalization is integrated into Xception, contributing to stable and faster convergence during training. It normalizes the inputs of each layer, reducing internal covariate shifts and aiding in the optimization process.
4. Transfer Learning:
— Xception often serves as a powerful base for transfer learning. Pre-trained on large datasets like ImageNet, Xception’s learned features can be fine-tuned for specific tasks with smaller datasets, demonstrating its versatility across various applications.
Xception’s architecture, centred around depthwise separable convolutions, and its strategic approaches contribute to its success in the realm of deep learning. As a scalable and efficient model, Xception continues to be influential in computer vision tasks and serves as a testament to the ongoing evolution of neural network architectures.
