Discover how convolutional neural networks optimize parameter efficiency and prevent overfitting in deep learning models for computer vision, providing a robust strategy for developers.
Table of Contents
Question
You input images with dimensions of 16x16x3 into a standard neural network, where each fully connected neuron in the first hidden layer has 768 weights. As the number of neurons and the size of the images increase, the model’s structure rapidly accumulates many parameters, leading to overfitting. What strategy would you implement to prevent overfitting while efficiently using the parameters?
A. Replace the rectified linear unit with the sigmoid activation function in the last fully connected layer.
B. Replace the rectified linear unit with the tanh activation function in the last fully connected layer.
C. Replace the standard neural network with a convolutional neural network.
D. Replace the standard neural network with a recurrent neural network.
Answer
C. Replace the standard neural network with a convolutional neural network.
Explanation
Overfitting in standard neural networks arises when every neuron in a fully connected layer learns weights from all pixels, leading to an enormous number of parameters. In the example provided, each neuron in the first hidden layer has 768 weights for 16×16×3 images. As image dimensions and neuron counts increase, the number of parameters grows rapidly, causing the model to memorize the training data including noise, rather than generalizing well on unseen data.
Key Reasons to Use Convolutional Neural Networks (CNNs):
- Local Receptive Fields: CNNs connect each neuron to only a small region of the input, capturing local features and reducing the total number of learnable parameters.
- Weight Sharing: The same filter (or set of weights) is used across different regions of the image, significantly cutting down the parameter count.
- Pooling Operations: Pooling layers reduce the spatial dimensions of the data, further cutting down complexity and the likelihood of overfitting.
By replacing a standard fully connected neural network with a convolutional neural network (as described in option C), the model leverages these architectural efficiencies, making it less prone to overfitting even as the number and size of input images increase.
Final Answer: C. Replace the standard neural network with a convolutional neural network.
This strategy efficiently combats parameter explosion and overfitting by ensuring that the model focuses on extracting meaningful spatial features, which is crucial in computer vision tasks.
Computer Vision for Developers skill 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 Computer Vision for Developers exam and earn Computer Vision for Developers certification.