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By simplifying the network architecture
By reformulating layers to learn residual functions
By reducing the number of layers
By eliminating the need for optimization techniques
Understanding the Answer
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Residual learning lets each block learn a small difference, called a residual, between its input and output. Other options are incorrect because Some think residuals simplify the whole network; A common mistake is to think residuals reduce the number of layers.
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Residual Learning Framework
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Definition
Residual learning framework is a technique used to train deeper neural networks more effectively by reformulating layers as learning residual functions with reference to layer inputs. This approach aims to address the optimization challenges associated with increasing network depth, enabling improved accuracy with significantly deeper networks.
Topic Definition
Residual learning framework is a technique used to train deeper neural networks more effectively by reformulating layers as learning residual functions with reference to layer inputs. This approach aims to address the optimization challenges associated with increasing network depth, enabling improved accuracy with significantly deeper networks.
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