Qu'est-ce que ResNet ?
ResNet, ou Réseau Résiduel, est un type d'apprentissage artificiel l'architecture des réseaux neuronaux that was introduced by researchers at Microsoft in 2015. It is designed to tackle the problem of training very deep réseaux neuronaux en utilisant une structure unique appelée connexions résiduelles.
Traditionnel apprentissage profond models often struggle with the vanishing gradient problem, where gradients (used to update the model’s weights) become too small for effective learning as they propagate back through many layers. ResNet addresses this issue by allowing gradients to flow through the network without being diminished, thanks to its skip connections.
A residual connection skips one or more layers and feeds the output of a previous layer directly into a later one. This means that the network can learn an ‘identity function’ if it needs to, which helps preserve information and makes it easier to train deeper networks. ResNet architectures can have hundreds or even thousands of layers, significantly improving their performance on tasks such as classification d'image et détection d'objets.
ResNet achieved remarkable results in various competitions, including the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) in 2015, where it won first place. The architecture is now widely used in many applications beyond image processing, including traitement du langage naturel et apprentissage par renforcement.
Dans l'ensemble, ResNet a eu un impact significatif sur le domaine de l'apprentissage profond en démontrant que des réseaux plus profonds peuvent être entraînés avec succès et utilisés pour atteindre des performances de pointe.