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residualneuralnetwork

residual neural network

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RESIDUAL NEURAL NETWORK - A residual neural network is an artificial neural network (ANN) of a kind that builds on constructs known from pyramidal cells in the cerebral cortex...

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Residual neural network description
A residual neural network is an artificial neural network (ANN) of a kind that builds on constructs known from pyramidal cells in the cerebral cortex. Residual neural networks do this by utilizing skip connections or short-cuts to jump over some layers. In its limit as ResNets it will only skip over a single layer. With an additional weight matrix to learn the skip weights it is referred to as HighwayNets. With several parallel skips it is referred to as DenseNets. In comparison, a non-residual neural network is described as a plain network in the context of residual neural networks.* The brain has structures similar to residual nets, as cortical layer VI neurons gets input from layer I, skipping over all intermediary layers. In the figure (below, on the right), this compares to signals from the (3) apical dendrite skipping over layers, while the (2) basal dendrite collecting signals from the previous and/or same layer. Similar structures exists for other layers. How many layers in the cerebral cortex compare to layers in an artificial neural network is not clear, neither if every area in cerebral cortex exhibits the same structure, but over large areas they look quite similar. * One motivation for skipping over layers in ANNs is to avoid the problem of vanishing gradients by reusing activations from a previous layer until the layer next to the current one have learned its weights. During training the weights will adapt to mute the previous layer and amplify the layer next to the current. In the simplest case only the weights for the connection to the next to the current layer is adapted, with no explicit weights for the upstream previous layer. This usually works properly when a single non-linear layer is stepped over, or in the case when the intermediate layers are all linear. If not, then an explicit weight matrix should be learned for the skipped connection. * The intuition on why this works is that the neural network collapses into fewer layers in the initial phase, which makes it easier to learn, and thus gradually expands the layers as it learns more of the feature space. During later learning, when all layers are expanded, it will stay closer to the manifold and thus learn faster. A neural network without residual parts will explore more of the feature space. This makes it more vulnerable to small perturbations that cause it to leave the manifold altogether, and require extra training data to get back on track.

Residual neural network description

A residual neural network is an artificial neural network (ANN) of a kind that builds on constructs known from pyramidal cells in the cerebral cortex. Residual neural networks do this by utilizing skip connections or short-cuts to jump over some layers. In its limit as ResNets it will only skip over a single layer. With an additional weight matrix to learn the skip weights it is referred to as HighwayNets. With several parallel skips it is referred to as DenseNets. In comparison, a non-residual neural network is described as a plain network in the context of residual neural networks.* The brain has structures similar to residual nets, as cortical layer VI neurons gets input from layer I, skipping over all intermediary layers. In the figure (below, on the right), this compares to signals from the (3) apical dendrite skipping over layers, while the (2) basal dendrite collecting signals from the previous and/or same layer. Similar structures exists for other layers. How many layers in the cerebral cortex compare to layers in an artificial neural network is not clear, neither if every area in cerebral cortex exhibits the same structure, but over large areas they look quite similar.
* One motivation for skipping over layers in ANNs is to avoid the problem of vanishing gradients by reusing activations from a previous layer until the layer next to the current one have learned its weights. During training the weights will adapt to mute the previous layer and amplify the layer next to the current. In the simplest case only the weights for the connection to the next to the current layer is adapted, with no explicit weights for the upstream previous layer. This usually works properly when a single non-linear layer is stepped over, or in the case when the intermediate layers are all linear. If not, then an explicit weight matrix should be learned for the skipped connection.
* The intuition on why this works is that the neural network collapses into fewer layers in the initial phase, which makes it easier to learn, and thus gradually expands the layers as it learns more of the feature space. During later learning, when all layers are expanded, it will stay closer to the manifold and thus learn faster. A neural network without residual parts will explore more of the feature space. This makes it more vulnerable to small perturbations that cause it to leave the manifold altogether, and require extra training data to get back on track.

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