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Residual Learning Framework
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A team of researchers is developing a new convolutional neural network for classifying images of various objects. They notice that as they add more layers to the network, the accuracy begins to stagnate or even decrease. How can the team utilize the residual learning framework to improve their model's performance?

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A

By adding more layers without any adjustments to the architecture.

B

By reformulating layers to learn residual functions that reference the inputs.

C

By completely removing layers to simplify the model.

D

By using a different activation function throughout the network.

Understanding the Answer

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Residual learning lets each block learn the difference between its input and the desired output. Other options are incorrect because Adding more layers without changing the design makes the learning signal weaker; Removing layers reduces the model's ability to learn complex patterns.

Key Concepts

Residual Learning Framework
Neural Network Optimization
Deep Learning Techniques
Topic

Residual Learning Framework

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easy level question

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understand

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Definition
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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