residual layers
Residual layers refer to a type of building block used in deep neural networks that enable the network to learn the residuals or differences between input and output, thus capturing the residual information. These layers involve adding shortcut connections, allowing the network to bypass certain layers and retain original input information. Residual layers help in improving the training process and addressing the vanishing gradient problem, enhancing the network's ability to learn and achieve better performance.
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