HomeIdentity Mapping in Deep Models
📚 Learning Guide
Identity Mapping in Deep Models
easy

A team of developers is working on a very deep neural network for image classification. They notice that as they add more layers, the training accuracy starts to decrease. To address this issue, they decide to implement identity mapping. How does this technique help improve the training process in their model?

Master this concept with our detailed explanation and step-by-step learning approach

Learning Path
Learning Path

Question & Answer
1
Understand Question
2
Review Options
3
Learn Explanation
4
Explore Topic

Choose AnswerChoose the Best Answer

A

It allows the network to skip layers, maintaining performance even with added depth.

B

It reduces the number of parameters in the model, simplifying the computations.

C

It forces the network to learn new representations at every layer, enhancing feature extraction.

D

It decreases the overall complexity of the network, making it easier to train.

Understanding the Answer

Let's break down why this is correct

Identity mapping adds a shortcut that copies the input directly to later layers. Other options are incorrect because The shortcut does not cut the number of weights; Identity mapping does not force each layer to learn new features.

Key Concepts

Deep Neural Networks
Identity Mapping
Residual Learning
Topic

Identity Mapping in Deep Models

Difficulty

easy level question

Cognitive Level

understand

Deep Dive: Identity Mapping in Deep Models

Master the fundamentals

Definition
Definition

Identity mapping is a technique used in constructing deeper models by adding layers that maintain the identity mapping from shallower models. This approach helps alleviate optimization challenges associated with increasing network depth and can lead to improved training error rates in very deep neural networks.

Topic Definition

Identity mapping is a technique used in constructing deeper models by adding layers that maintain the identity mapping from shallower models. This approach helps alleviate optimization challenges associated with increasing network depth and can lead to improved training error rates in very deep neural networks.

Ready to Master More Topics?

Join thousands of students using Seekh's interactive learning platform to excel in their studies with personalized practice and detailed explanations.

Start Learning with SeekhExplore More Topics