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Computer Vision Systems • Data Augmentation (AutoAugment, Mixup, Synthetic Data)Hard⏱️ ~2 min

Failure Modes and Edge Cases in Data Augmentation

MIXUP BREAKS OBJECT DETECTION
Naive Mixup blends two images, creating overlapping objects with contradictory bounding boxes. If image A has a car at [10,20,100,80] and image B has a truck at [50,30,150,100], the blended image has neither object correctly located. IoU (Intersection over Union) drops 5-15 percentage points. Fix: Use CutMix instead, which pastes a rectangular patch from one image onto another, keeping bounding boxes intact for the unoccluded regions.
OVER-REGULARIZATION SYMPTOMS
Combining strong augmentation with other regularizers (Mixup α>0.4 + label smoothing + heavy RandAugment + dropout) can prevent learning. Symptoms: training accuracy plateaus below validation accuracy, convergence slows 50-100%, final accuracy is 1-3 percentage points lower than optimal. Fix: Reduce augmentation strength. If training accuracy is significantly below validation, you are over-regularizing.
DOMAIN-SPECIFIC FAILURES
Medical imaging: Color carries diagnostic signal (skin lesion redness indicates inflammation). Heavy color jitter destroys this information.
Text recognition: Rotation beyond ±5° makes characters unreadable.
Audio spectrograms: Time stretching distorts frequency relationships.
Always validate augmentation effects on domain experts before deploying.
⚠️ Key Trade-off: Augmentations that help natural image classification may actively hurt specialized domains. No policy is universally correct.
AUTOAUGMENT PROXY OVERFITTING
Policies discovered on 10% data subsets or small proxy models (5 epochs) may not transfer to full-scale training. A policy showing 2% improvement on proxy might show 0% or negative transfer at full scale. Always validate discovered policies on held-out data slices at full training scale before production use.

💡 Key Takeaways

✓Naive Mixup breaks detection: overlapping objects with contradictory boxes drop IoU 5-15 percentage points; use CutMix instead

✓Over-regularization: training accuracy below validation, slow convergence, 1-3 percentage points accuracy loss

✓Domain-specific failures: color jitter hurts medical imaging, rotation breaks text recognition

✓AutoAugment proxy overfitting: policies from small subsets may show zero or negative transfer at full scale

📌 Interview Tips

1Describe the Mixup detection problem: blended bounding boxes are invalid; CutMix preserves boxes in unoccluded regions

2Explain over-regularization diagnosis: if training accuracy is below validation, reduce augmentation strength

3Mention domain-specific considerations: color matters in medical, rotation limits for text, validate with domain experts

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