Transfer Learning Fine-Tuning - Interactive Visualization

Transfer learning reuses weights trained on a large dataset (like ImageNet) as a starting point for a new task. Early network layers learn universal features - edges, textures, color gradients - that transfer across almost any visual task. By freezing these layers and only fine-tuning the later task-specific layers, you can achieve strong performance with a fraction of the training data.

• See which layers are frozen (gray) vs trainable (colored) and how gradients flow only through unfrozen portions
• Understand the typical fine-tuning recipe: freeze all but the last few layers, train on small dataset, then progressively unfreeze
• Compare training from scratch vs fine-tuning on a small dataset - fine-tuning converges faster and to a better optimum
• Learn progressive unfreezing: gradually thaw layers from the top down to avoid destroying pretrained features
• Understand when NOT to transfer: if source and target domains are very different, pretrained features may hurt performance

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