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?

Question

Seekh · Accepted Answer

Identity mapping lets each layer simply pass its input straight through when the new layer’s weights are not yet useful, so the network behaves like a shallow model until the deeper layers learn something useful. By adding shortcut connections that skip layers, the gradient can travel back without vanishing or exploding, keeping the learning signal strong for every layer. As a result, deeper networks can train more reliably because earlier layers are not forced to learn redundant transformations, and the overall loss surface becomes smoother. For example, a 100‑layer network with identity shortcuts can still achieve the same accuracy as a 10‑layer network while allowing the extra layers to refine features rather than fight for gradient flow. This technique therefore stabilizes training and lets the model grow deeper without losing accuracy.

Learning Path

Choose the Best Answer

Understanding the Answer

Answer

Detailed Explanation

Key Concepts

Practice Similar Questions

In the context of deep learning, how can identity mapping enhance business applications while facilitating the scaling of models?

A team of researchers is developing a deep neural network for image recognition, but they notice that the network struggles to learn effectively as they increase the number of layers. Which of the following strategies would best address the vanishing/exploding gradients problem they are facing?

How does identity mapping in deep models primarily benefit the training of neural networks?

How does identity mapping in deep neural networks help with optimization during training?

Identity Mapping in deep learning models is to optimizing training as a navigation system is to _____?

Arrange the following steps in the process of implementing identity mapping in deep neural networks to optimize training: A) Add identity mapping layers, B) Train the network with residual connections, C) Initialize weights for deeper layers, D) Evaluate performance on training data.

You are designing a deep neural network to improve image recognition accuracy. Which of the following strategies would best utilize identity mapping to address common issues of training deep networks?

In the context of deep learning, identity mapping is primarily used to maintain the ________ from shallower models, aiding in the training of deeper networks.

Why does identity mapping in deep neural networks help improve training performance in very deep models?

Ready to Master More Topics?