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Computer Vision Systems • Data Augmentation (AutoAugment, Mixup, Synthetic Data)Medium⏱️ ~3 min

AutoAugment: Automated Policy Discovery

THE MANUAL TUNING PROBLEM
Hand-tuning augmentation policies is tedious and error-prone. Should you rotate ±15° or ±30°? Apply color jitter with range 0.2 or 0.4? These decisions are dataset-specific: what works for natural images may hurt medical imaging where color carries diagnostic signal. AutoAugment automates this by searching over the space of possible policies.
HOW AUTOAUGMENT WORKS
AutoAugment defines a search space of augmentation policies. Each policy consists of 5-25 sub-policies. Each sub-policy contains 2 operations (e.g., rotate, color jitter) with two parameters: probability of applying (0-1) and magnitude (how strong). The search algorithm (reinforcement learning or population-based training) evaluates thousands of candidate policies by training small proxy models for 5-10 epochs each. Policies that improve validation accuracy are refined; poor ones are discarded.
SEARCH COST AND AMORTIZATION
Initial search is expensive: 12-48 hours on a small GPU cluster. But the discovered policy is reusable. A policy found on one architecture (ResNet-50) transfers well to others (EfficientNet) on the same dataset. Store policies as versioned artifacts. The one-time search cost is amortized across all future training runs.
⚠️ Key Trade-off: Policies discovered on small proxy models (10% data, 5 epochs) may not transfer perfectly to full-scale training. Validate on held-out slices before production use.
ACCURACY IMPROVEMENTS
AutoAugment typically improves top-1 accuracy by 1-2 percentage points on large datasets (ImageNet) and 2-5 percentage points on smaller datasets (CIFAR-10) where overfitting is more severe.

💡 Key Takeaways

✓AutoAugment searches over augmentation policies (5-25 sub-policies, each with 2 operations, probability, and magnitude)

✓Search cost: 12-48 hours evaluating thousands of policies with proxy models trained 5-10 epochs each

✓Discovered policies transfer across architectures on the same dataset, amortizing the one-time search cost

✓Accuracy improvement: 1-2 percentage points on ImageNet, 2-5 percentage points on smaller datasets

📌 Interview Tips

1Explain the policy structure: 5-25 sub-policies, each with 2 operations and probability/magnitude parameters

2Mention transferability: policy found on ResNet-50 works for EfficientNet on the same dataset

3Warn about proxy model overfitting: policies from small subsets may not transfer to full-scale training

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