AI Termcirca 1993· Added May 30, 2026
Transfer Learning
Transfer learning is the practice of reusing a pre-trained model on a new, related task.
Transfer learning involves leveraging a model trained on one problem to help solve another similar problem. This approach can drastically reduce the required training data and time for the new task. By starting with an already knowledgeable model, often trained on extensive datasets, you can fine-tune it to specific needs with less data. Transfer learning is especially prevalent in natural language processing and computer vision.
Examples
- Using BERT pre-trained on a vast corpus to tackle sentiment analysis tasks with minimal additional data.
- Fine-tuning ImageNet models for specific image classification tasks like identifying defects in manufacturing.
Common misconceptions
- Transfer learning is not the same as multi-task learning; it focuses on transferring knowledge from one domain to another.
- It doesn't require starting from scratch; models are adapted rather than built anew.
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