Simulation 1: Neural Architecture Pipeline
Select a base model and observe the feature extraction pipeline. Watch how high-level features are distilled from raw pixels.
Simulation 2: Feature Extraction – Hierarchical Learning
Explore what a CNN learns at each layer — from low-level edges to high-level flower shapes. Compare random, pretrained, and fine-tuned representations.
Simulation 3: Freezing vs Fine-Tuning – Training Dynamics
Adjust freezing, learning rate, and dataset size to see how they affect training and validation curves. Watch for overfitting!
Train Accuracy
Validation Accuracy
Train Loss
Validation Loss
Simulation 4: Representation Space – Embedding Geometry
Watch how flower embeddings evolve from chaotic blobs to tight, separable clusters as the model trains. Compare random, pretrained, and fine-tuned representations.
Manifold Learning
Wait for animation or select a mode to explore conceptual insights.
Clustering Geometry
Higher separability indicates the model has learned distinct features for each class, enabling linear separation in high-dimensional space.
Simulation 5: Gradient Flow – Stability Explorer
See how gradients flow through frozen vs trainable layers. Understand why low learning rates prevent catastrophic forgetting in deep networks.
Layer-wise Gradient Magnitude
Layer-wise Weight Changes
Gradient Flow
Gradient flow determines how much each layer learns. If gradients are too small (vanishing) or too large (exploding), training becomes unstable.
Weight Dynamics
Large weight changes in pretrained layers can lead to "Catastrophic Forgetting," where the model loses its general feature extraction capabilities.
Stability Meter
Catastrophic Forgetting Risk: Low
Simulation 6: Domain Similarity – Strategy Decision Lab
Explore when transfer learning works best. Adjust domain similarity, data size, and compute budget to see real-time strategy recommendations.
Domain Scenario
Adjust the sliders to see a personalized transfer learning strategy recommendation.
Strategy Matrix
Retrain
Extractor
Tuning