A connexion résiduelle is a technique used in apprentissage profond, particularly in réseaux neuronaux, to help improve their training and performance. The concept was popularized by the ResNet (Residual Network) architecture, which won the ImageNet compétition en 2015.
In a typical neural network, data flows sequentially through layers, where each layer applies certain transformations. However, as networks become deeper (with more layers), they can experience issues such as la disparition du gradient, where the gradients used to update weights during training become very small, hindering learning.
Les connexions résiduelles résolvent ce problème en permettant à l'entrée d'une couche de contourner une ou plusieurs couches et d'être ajoutée directement à la sortie de ces couches. Cela est représenté mathématiquement comme :
Sortie = F(Entrée) + Entrée
Here, F(Input) represents the transformation applied by the layers being bypassed. By including the original input in the output, residual connections help maintain the flow of information and gradients, making it easier for the network to learn complex motifs.
These connections also allow for the training of much deeper networks, leading to better performance on various tasks like image recognition, traitement du langage naturel, and more. Overall, residual connections are a crucial innovation in modern deep learning, facilitating the development of more sophisticated AI models.