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Answer
Increasing the depth of a neural network does not guarantee better image classification performance because deeper models can overfit, especially when the dataset is small or noisy, and they may suffer from vanishing gradients or training instability. A deeper network can learn more complex features, but if the data does not contain enough variation or if the network is too large, the extra layers simply memorize training examples instead of learning general patterns. For example, a 50‑layer network trained on a tiny dataset of handwritten digits may perform worse than a shallow 5‑layer network because the extra layers overfit to the few training images. Thus, depth must be matched to the dataset size, quality, and the problem’s complexity, and regularization or architectural tricks are often needed to reap the benefits of deeper models.
Detailed Explanation
Adding more layers can let a network learn more complex patterns, but only if the data is enough and varied. Other options are incorrect because The mistake is thinking that more layers always help.
Key Concepts
Network Depth
Image Classification
Overfitting
Topic
Network Depth Importance
Difficulty
medium level question
Cognitive Level
understand
Practice Similar Questions
Test your understanding with related questions
1
Question 1A neural network architecture is being designed for an image recognition task. Considering the importance of network depth, which of the following approaches would most likely enhance the model's performance, particularly in feature integration and classification accuracy?
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Question 2Why is increasing the depth of a neural network often beneficial for visual recognition tasks?
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Question 3What is the primary reason deeper neural networks tend to improve performance in visual recognition tasks?
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4
Question 4Arrange the following steps in the correct order to explain the importance of network depth in neural networks for visual recognition tasks: A) Network depth increases the capacity for feature extraction, B) Deeper networks can integrate complex features more effectively, C) The model achieves higher classification accuracy, D) Training becomes more challenging due to vanishing gradients.
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Question 5Why does increasing the depth of a neural network generally improve its performance in visual recognition tasks?
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6
Question 6In the context of neural networks, increasing the _____ of a model generally improves its ability to integrate features and enhance classification accuracy in visual recognition tasks.
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Question 7Why does increasing the depth of a neural network often lead to performance degradation despite not being caused by overfitting?
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8
Question 8A data scientist is tasked with building a deep neural network to classify images of animals. They notice that as they increase the depth of the network, the accuracy of their model begins to degrade significantly. What is the most likely reason for this degradation, and what approach could they take to mitigate it?
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Question 9Why does increasing the depth of a neural network sometimes lead to worse performance, despite having more parameters?
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