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Layer Transferability Explorer

Interactive 3D transfer-learning simulator: pick the split layer between a frozen pretrained backbone and a freshly trained head, tune target-dataset size and fine-tuning, and watch the classic transferability curve — including the mid-network co-adaptation dip — respond live.

Machine Learning & Neural Networks3DAdvanced60 FPS📱 Mobile-adapted⇄ 2D version

This simulator visualizes a ten-layer neural network as a 3D stack and lets you choose exactly where to cut it for transfer learning: everything below the split stays frozen with its pretrained weights, everything above is replaced and trained fresh on a target task. Drag the split-layer slider and the target-dataset-size slider to watch a live, formula-driven accuracy curve respond — including the characteristic mid-network dip caused by broken co-adaptation between frozen and retrained layers, first documented by Yosinski et al. Toggle fine-tuning to see that dip mostly close, and overlay a random-initialization baseline to see exactly how much transfer learning is worth at any dataset size.

⚙ Under the hood

Pick the split layer between a frozen pretrained backbone and a freshly trained head in a 3D ten-layer network, and watch a live, formula-driven accuracy curve reveal the mid-network co-adaptation dip from classic transfer-learning research.

transfer learningneural networksmachine learningfine-tuningdeep learning

3D · Three.js / WebGL renderer · 60 FPS target · runs fully client-side, no install