Transfer Learning

term_id: transfer_learning

Category: training_techniques

Definition

Transfer learning leverages pre-trained models to improve performance and reduce training time on new, related tasks. Instead of training from scratch, developers fine-tune existing weights, allowing the model to adapt quickly to specific datasets. This approach is particularly valuable when labeled data is scarce, as it capitalizes on general features learned from large-scale source domains, such as ImageNet for computer vision or large text corpora for NLP.

Summary

A machine learning technique where a model developed for one task is reused as the starting point for a model on a second task.

Key Concepts

  • Pre-trained Models
  • Fine-tuning
  • Domain Adaptation
  • Feature Extraction

Use Cases

  • Image classification with limited data
  • Sentiment analysis on niche topics
  • Medical diagnosis assistance

Code Example

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from transformers import AutoModelForSequenceClassification
model = AutoModelForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)

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