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Week 6 Summary: Data scarcity & transfer learning
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Week 6 Summary: Data scarcity & transfer learning

Cross-Book Summary

1. The Small Data Challenge (Sandfeld Ch 11, Neuer Ch 4)

  • Experimental Cost: Unlike internet-scale data, scientific datasets are expensive. A model with millions of parameters (like a CNN) will easily “memorize” a small materials dataset rather than learning the underlying physics.
  • Generalization: To avoid overfitting, we must use techniques like early stopping, dropout, and robust train/val/test splitting (Neuer Ch 4.2.1).

2. Transfer Learning (McClarren Ch 6, Sandfeld Ch 19)

  • Leveraging Pretrained Models: Instead of training from scratch, we start with a model (e.g., ResNet, EfficientNet) trained on massive datasets like ImageNet.
  • Feature Reuse: The “early” layers of a CNN learn universal visual primitives (edges, blobs, textures) that are surprisingly effective for micrographs, even if the model was originally trained on natural objects like dogs or cars.
  • Fine-Tuning Strategies:
    • Feature Extraction: Freeze the backbone and train only the final classification head.
    • Fine-Tuning: Unfreeze the last few convolutional blocks and train with a very low learning rate.
  • Case Study (McClarren): Transferring EfficientNet features to detect volcanoes on Venus—a scientific task with limited labeled data.

3. Data Augmentation

  • Artificial Scarcity Relief: Increasing the effective dataset size by applying transformations: rotation, flipping, cropping, and color jittering.
  • Physics-Preserving Augmentation: In materials science, rotations and flips often preserve the underlying physical meaning (unless there is a macro-scale gradient or oriented growth), unlike in natural images where “upside-down” objects are rare.

90-Minute Lecture Strategy (50 Slides)

Part 1: The Curse of Small Data (Slides 1-10)

  • Why materials data is “Small, Expensive, and Messy.”
  • The risk of overfitting: When your model learns the specific scratches on a sample instead of the phase boundaries.

Part 2: Fighting Scarcity with Augmentation (Slides 11-20)

  • Traditional Augmentation: Flips, Rotations, Zooms.
  • Advanced Augmentation: Mixup, Cutmix.
  • Domain-specific Augmentation: Adding simulated noise or artifacts.

Part 3: Transfer Learning Mechanics (Slides 21-35)

  • The concept of “Feature Hierarchies.”
  • Pretrained Models: An overview of available backbones.
  • How to implement Transfer Learning: Freezing, Unfreezing, and Learning Rates.
  • Case Study: ImageNet to SEM images.

Part 4: Domain Shift in Science (Slides 36-45)

  • Does ImageNet pretraining always help? (Comparing natural vs. scientific domains).
  • Cross-Material Transfer: Training on Steel and fine-tuning on Aluminum.
  • Synthetic-to-Real Transfer: Pretraining on simulations (Phase Field, FEM).

Part 5: Summary & Best Practices (Slides 46-50)

  • Selecting the right backbone.
  • Validation strategies for small data.
  • Discussion: Is “Learning to Learn” the future of Materials Science?

Quarto Website Update (Summary)

Summary for ML-PC Week 6:
This unit addresses the fundamental bottleneck of materials informatics: Data Scarcity. We explore how to build powerful deep learning models when only a few hundred labeled images or signals are available. The core focus is on Transfer Learning, where we leverage knowledge from models pretrained on millions of natural images to accelerate learning and improve generalization on materials tasks. We also cover Data Augmentation strategies tailored for scientific data and discuss when and why transferring knowledge across different physical domains succeeds or fails.