Comparer des méthodes

	Perceptron multicouche affiné	Perceptron multicouche (MLP)
Domaine	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	1986 (MLP); fine-tuning practice formalised c. 2014	1986
Auteur d'origine	Rumelhart, Hinton & Williams (MLP); Yosinski et al. (fine-tuning analysis)	Rumelhart, D. E.; Hinton, G. E.; Williams, R. J.
Type	Supervised deep learning with pre-trained weight initialisation	Supervised feedforward neural network
Source fondatrice	Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323, 533–536.	Rumelhart, D. E., Hinton, G. E. & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323, 533–536.
Alias	fine-tuned MLP, adapted MLP, domain-adapted multilayer perceptron, MLP fine-tuning	MLP, feedforward neural network, fully connected neural network, vanilla neural network
Apparentées	4	4
Résumé	A Fine-Tuned Multilayer Perceptron starts from weights learned on a source task — or a large general-purpose dataset — and continues training on a smaller target dataset with a reduced learning rate. This reuse of pre-learned representations allows the MLP to converge faster and generalise better than training from scratch, especially when labelled target data is scarce.	A Multilayer Perceptron is a classic fully connected feedforward neural network trained with the backpropagation algorithm, as formalised by Rumelhart, Hinton & Williams in their landmark 1986 Nature paper. Composed of an input layer, one or more hidden layers of neurons, and an output layer, the MLP learns nonlinear mappings from input features to target outputs and serves as the foundational building block of modern deep learning.