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Transfer learning is one of the most powerful and practical techniques in deep learning. Instead of training a model from scratch, you start with a model that has already been trained on a large dataset and fine-tune it for your specific task. This dramatically reduces the amount of data and compute needed.
| Challenge | Without Transfer Learning | With Transfer Learning |
|---|---|---|
| Data requirements | Need millions of labelled examples | A few hundred or thousand examples may suffice |
| Training time | Days or weeks on GPUs | Hours or less |
| Compute cost | Extremely expensive | Affordable |
| Performance | Often suboptimal with limited data | Often state-of-the-art even with small datasets |
Deep learning models learn hierarchical features. In a CNN trained on ImageNet:
The universal features learned in early and middle layers are useful for virtually any vision task. Transfer learning reuses these features.
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