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How can transfer learning improve the performance of a neural network on a small dataset?

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How can transfer learning improve the performance of a neural network on a small dataset?

Asked on Jan 30, 2026

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Transfer learning can significantly enhance the performance of a neural network on a small dataset by leveraging pre-trained models. These models, trained on large datasets, can be fine-tuned to adapt to new tasks with limited data, thus improving accuracy and reducing training time.

Example Concept: Transfer learning involves using a pre-trained neural network as a starting point for a new task. The lower layers of the network, which capture general features, are retained, while the upper layers are fine-tuned to the specific task at hand. This approach allows the model to benefit from the knowledge gained from the large dataset, even when only a small dataset is available for the new task.

Additional Comment:

Transfer learning is particularly effective in domains where labeled data is scarce.
Commonly used pre-trained models include VGG, ResNet, and BERT, depending on the task (e.g., image or text).
Fine-tuning involves adjusting the weights of the upper layers while keeping the lower layers fixed or minimally adjusted.
This technique reduces the risk of overfitting, which is a common issue with small datasets.

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How can transfer learning improve the performance of a neural network on a small dataset?

Asked on Jan 30, 2026

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