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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.
He is an associate professor at Massachusetts Institute of Technology and is known as one of the creators of residual neural network (ResNet). [ 1 ] [ 3 ] Early life and education
Every residual connection is a skip connection, but almost all skip connections are not residual connections. The original Highway Network paper [ 16 ] not only introduced the basic principle for very deep feedforward networks, but also included experimental results with 20, 50, and 100 layers networks, and mentioned ongoing experiments with up ...
In May 2015, Rupesh Kumar Srivastava, Klaus Greff, and Schmidhuber used LSTM principles to create the highway network, a feedforward neural network with hundreds of layers, much deeper than previous networks. [8] [31] [32] In Dec 2015, the residual neural network (ResNet) was published, which is a variant of the highway network. [30] [33]
In 2015, two techniques were developed concurrently to train very deep networks: highway network [102] and residual neural network (ResNet). [103] The ResNet research team attempted to train deeper ones by empirically testing various tricks for training deeper networks until they discovered the deep residual network architecture. [104]
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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.