What is Transfer Learning

How It Works

Transfer learning takes a model that has been pretrained on a large dataset (like ImageNet for vision or BookCorpus for text) and adapts it for a new task. The pretrained model has learned general features (edges, textures, syntax, semantics) that transfer well to related tasks. Adaptation typically involves replacing the final classification layer and fine-tuning part or all of the network on task-specific data.

Technical Details

Common strategies include feature extraction (freeze pretrained weights, train only the new head), full fine-tuning (update all weights), and gradual unfreezing (progressively unfreeze layers from top to bottom). Learning rates for pretrained layers are typically 10-100x smaller than for new layers. Pretrained models from model hubs (Hugging Face, timm) provide ready-to-use starting points for virtually any vision, language, or multimodal task.

Best Practices

• Start with feature extraction and only move to full fine-tuning if performance is insufficient
• Use lower learning rates for pretrained layers to avoid catastrophic forgetting
• Select a pretrained model trained on data similar to your domain when possible
• Validate that transfer provides benefit over training from scratch for your data size

Common Pitfalls

• Fine-tuning with too high a learning rate, destroying useful pretrained features
• Assuming larger pretrained models always transfer better when domain fit matters more
• Not unfreezing enough layers when the target domain is very different from pretraining data
• Ignoring the computational cost of fine-tuning very large pretrained models

Advanced Tips

• Use cross-modal transfer learning to apply language model knowledge to multimodal tasks
• Implement adapter modules or LoRA for parameter-efficient transfer without modifying base weights
• Apply progressive transfer through intermediate tasks for distant domain adaptation
• Use transfer learning for multimodal feature extractors that bridge vision, language, and audio