Transfer learning in artificial neural networks applies knowledge gained from solving one problem to a similar but different problem, leading to faster convergence and lower loss during training.
Using the same layers and layer sizes in original and transfer-learning enhanced models simplifies transfer learning, ensuring relevant information is retained, as seen in experiments modeling guitar effects and amps.
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