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A residual neural network (also referred to as a residual network or ResNet) [1] is a deep learning architecture in which the layers learn residual functions with reference to the layer inputs. It was developed in 2015 for image recognition , and won the ImageNet Large Scale Visual Recognition Challenge ( ILSVRC ) of that year.
The codebase for AlexNet was released under a BSD license, and had been commonly used in neural network research for several subsequent years. [ 20 ] [ 17 ] In one direction, subsequent works aimed to train increasingly deep CNNs that achieve increasingly higher performance on ImageNet.
Residual connections, or skip connections, refers to the architectural motif of +, where is an arbitrary neural network module. This gives the gradient of ∇ f + I {\displaystyle \nabla f+I} , where the identity matrix do not suffer from the vanishing or exploding gradient.
Residual connections gave rise to their own weight initialization problems and strategies. Fixup initialization is designed specifically for networks with residual connections and without batch normalization, as follows: [18] Initialize the classification layer and the last layer of each residual branch to 0.
This page was last edited on 20 November 2017, at 05:18 (UTC).; Text is available under the Creative Commons Attribution-ShareAlike 4.0 License; additional terms may apply.
Neural architecture search (NAS) [1] [2] is a technique for automating the design of artificial neural networks (ANN), a widely used model in the field of machine learning.NAS has been used to design networks that are on par with or outperform hand-designed architectures.
Residential network, a computer network provided by a university to serve residence halls; Residual flow network, in graph theory; Residual neural network, a type of artificial neural network; Residential Energy Services Network (RESNET), an organization responsible for home energy ratings
Recurrent neural networks (RNNs) are a class of artificial neural network commonly used for sequential data processing. Unlike feedforward neural networks, which process data in a single pass, RNNs process data across multiple time steps, making them well-adapted for modelling and processing text, speech, and time series.