What is the difference between a Residual Neural Net and a Recurrent Neural Net?

As I understand,

Residual Neural Networks are very deep networks that implement 'shortcut' connections across multiple layers in order to preserve context as depth increases. Layers in a residual neural net have input from the layer before it and the optional, less processed data, from X layers higher. This prevents early or deep layers from 'dying' due to converging loss.

Recurrent Neural Networks are networks that contain a directed cycle which when computed is 'unrolled' through time. Layers in recurrent neural network have input from the layer before it and the optional, time dependent extra input. This provides situational context for things like natural language processing.

Therefor, a recurrent neural network can be used to generate a basic residual network if the input remains the same with respect to time.

Is this correct?


2 Answers 2


The answer is YES, they basically are the same according to this paper

enter image description here

The figure above shows how they compared both and how a ResNet can be reformulated into a recurrent form tat is almost identical to a RNN.
For more info you can read the paper and get deeper.


I believe this is true:

  • Recurrent neural networks (RNN) generally refer to the type of neural network architectures, where the input to a neuron can also include additional data input, along with the activation of the previous layer. E.g. for real-time handwriting or speech recognition.
  • Residual neural networks (ResNet) refer to another type of neural network architecture, where the input to a neuron can include the activations of two (or more) of its predecessors. E.g. for non-realtime handwriting or speech recognition.

So, there is a difference but maybe these implementations can be considered equivalent, especially if we are performing non-real-time data analysis.


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