Transfer Learning Revolution

Transfer learning uses knowledge from pre-trained models to solve new tasks with minimal data and compute. Build production models in days instead of months, achieving 90-95% of custom model performance with 1% of the data and compute.

Why Transfer Learning?

Benefits

• 10-50x Faster: Days instead of months to train
• 100x Less Data: 100-1K samples vs 100K-1M
• Better Performance: Pre-trained on billions of examples
• Lower Costs: ₹5L vs ₹50L for training from scratch

Computer Vision Transfer Learning

Pre-trained Models

• ResNet (50, 101, 152): Image classification backbone
• EfficientNet: Best accuracy/efficiency tradeoff
• Vision Transformer (ViT): Transformer for images
• CLIP: Vision-language model by OpenAI

Fine-tuning Strategies

• Feature Extraction: Freeze backbone, train classifier (fastest)
• Fine-tune Top Layers: Unfreeze last few layers
• Full Fine-tuning: Train all layers with low learning rate
• Progressive Unfreezing: Gradually unfreeze from top to bottom

NLP Transfer Learning

Pre-trained Language Models

• BERT: Bidirectional encoding for understanding
• GPT-3/4: Autoregressive for generation
• RoBERTa: Optimized BERT training
• T5: Text-to-text framework
• Domain-Specific: BioBERT, ClinicalBERT, FinBERT

Fine-tuning for NLP

• Classification: Add classification head, fine-tune
• NER: Token-level classification
• Question Answering: Span prediction
• Summarization: Seq-to-seq fine-tuning

Domain Adaptation

When Domains Differ

• Problem: Pre-trained on ImageNet, need medical imaging
• Solution: Domain adaptation techniques
• Methods:
  • Fine-tune on target domain data
  • Multi-task learning
  • Domain adversarial training
  • Self-supervised pre-training on unlabeled target data

Implementation Guide

Step 1: Choose Pre-trained Model

• Select based on task similarity and model size
• Hugging Face Model Hub: 200K+ models
• TensorFlow Hub, PyTorch Hub

Step 2: Prepare Data

• 100-1K labeled samples for simple tasks
• 1K-10K for complex tasks
• Match preprocessing to pre-training (normalization, augmentation)

Step 3: Fine-tune

• Use low learning rate (1e-5 to 1e-4)
• Train for 3-10 epochs
• Monitor validation performance
• Use early stopping

Step 4: Evaluate & Deploy

• Test on holdout set
• Compare to baseline and custom model
• Deploy with same inference pipeline

Case Study: Medical Imaging

• Task: X-ray disease classification (14 classes)
• Data: 1,000 labeled images (vs 100K for training from scratch)
• Model: EfficientNet-B4 pre-trained on ImageNet
• Fine-tuning: 5 epochs, 2 hours on single GPU
• Results:
  • Accuracy: 92% (vs 88% from scratch with 100K images)
  • Training time: 2 hours vs 2 weeks
  • Cost: ₹5L vs ₹60L

Common Pitfalls

• Too High Learning Rate: Destroys pre-trained weights
• Wrong Preprocessing: Must match pre-training
• Overfitting: Small dataset + large model
• Not Enough Fine-tuning: Feature extraction may not be enough

Tools & Frameworks

• Hugging Face Transformers: NLP models, easy fine-tuning
• TensorFlow Hub / PyTorch Hub: Pre-trained vision models
• timm (PyTorch Image Models): 500+ vision models
• FastAI: High-level API for transfer learning