Bloc résiduel inversé
An Inverted Residual Block is a key architectural component used primarily in mobile réseaux neuronaux, such as MobileNet. Its design aims to enhance l'efficacité computationnelle while maintaining high performance du modèle, particularly for tasks like classification d'image.
The concept of the Inverted Residual Block revolves around a few key operations. First, it employs a lightweight convolution séparables en profondeur, which splits the convolution operation into two simpler parts: a depthwise convolution (which applies a single filter per input channel) followed by a pointwise convolution (which combines the outputs of the depthwise convolution). This significantly reduces the computational load compared to traditional convolutions.
Un autre aspect important du Bloc Résiduel Inversé est le use of linear bottlenecks. In a typical block, the input is first expanded to a higher-dimensional space using a 1×1 convolution, then processed through the depthwise convolution, and finally reduced back to a lower-dimensional space with another 1×1 convolution. This ‘inverted’ structure allows for efficient processing, as it focuses on maintaining important features while discarding less relevant data.
The block also incorporates residual connections, allowing the input to bypass certain layers. This helps in preventing the vanishing gradient problem during training, making it easier to optimize deeper networks. Overall, the Inverted Residual Block is designed to maximize the performance of neural networks on mobile and edge devices, where ressources informatiques sont limités.