A Review of Deep Transfer Learning and Recent Advancements

Deep learning has been the answer to many machine learning problems during the past two decades. It investigates the DTL approaches by reviewing applied DTL techniques in the past five years and a couple of experimental analyses of DTLs to discover the best practice for using DTL in different scenarios.

⚡ This is an original paraphrased summary — not copied from the abstract. Full paper available at the source link below.

• 1 However, it comes with two significant constraints: dependency on extensive labeled data and training costs.
• 2 Transfer learning in deep learning, known as Deep Transfer Learning (DTL), attempts to reduce such reliance and costs by reusing obtained knowledge from a source data/task in training on a target data/task.
• 3 Most applied DTL techniques are network/model-based approaches.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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