Data Augmentation

Data augmentation creates synthetic training data from existing samples, boosting model performance by 10-30% without additional data collection. Critical when data is limited or expensive.

Computer Vision Augmentation

• Geometric: Rotation, flip, crop, zoom
• Color: Brightness, contrast, saturation adjustments
• Mixup: Blend two images and labels
• CutMix: Cut and paste image regions
• AutoAugment: Learn optimal augmentation policy

NLP Augmentation

• Back-translation: Translate to another language and back
• Synonym Replacement: Replace words with synonyms
• Random Insertion/Deletion: Add/remove words
• Paraphrasing: Rephrase sentences (GPT-based)
• Contextual Word Embeddings: BERT-based substitution

Audio Augmentation

• Time stretching, pitch shifting
• Add background noise
• Speed/tempo changes
• SpecAugment for speech recognition

Results

• 10-30% accuracy improvement with limited data
• Better generalization to real-world variations
• Reduced overfitting