Convolutional Neural Network CNN: What Is Auto Association Task?

Learn about the auto association task in neural networks, its purpose, and how it relates to input-output patterns. Understand the correct answer to this critical concept for CNN certification exams.

Question

What is auto association task in neural network?

A. input pattern keeps on changing
B. output pattern keeps on changing
C. input pattern has become static
D. None

Answer

D. None

Explanation

An auto-association task in neural networks refers to a process where the network is trained to reproduce its input patterns as output. This is a defining characteristic of auto-associative neural networks (AANNs), also commonly referred to as autoencoders. The goal of such networks is to learn an identity mapping between the input and output while possibly compressing the data into a lower-dimensional representation via a bottleneck layer.

Here’s why none of the options provided are correct:

Option A: “Input pattern keeps on changing”
This is incorrect because, in auto-association tasks, the input pattern does not “keep changing.” Instead, the network learns to replicate static input patterns as output.

Option B: “Output pattern keeps on changing”
This is also incorrect because the output pattern is expected to match the input pattern. The network’s purpose is to reconstruct the same pattern it receives as input.

Option C: “Input pattern has become static”
While input patterns used during training can be static, this option does not fully capture the essence of an auto-associative task, which focuses on reproducing identical outputs from given inputs.

Option D: “None”
This is the correct choice because none of the other options accurately describe what an auto-association task entails.

Key Characteristics of Auto-Associative Neural Networks (AANNs)

• Input Equals Output: The network learns to map each input vector to itself.
• Compression and Reconstruction: Often includes a bottleneck layer that compresses data into a lower-dimensional representation before reconstructing it.
• Applications: Used for dimensionality reduction, anomaly detection, and noise removal.
• Training Process: AANNs are trained using backpropagation or similar methods to minimize reconstruction error.

In conclusion, an auto-association task involves training a neural network to reproduce its input as output, making none of the provided options a complete or accurate description of this task.

Convolutional Neural Network CNN certification exam assessment practice question and answer (Q&A) dump including multiple choice questions (MCQ) and objective type questions, with detail explanation and reference available free, helpful to pass the Convolutional Neural Network CNN exam and earn Convolutional Neural Network CNN certification.