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Antigravity Agent
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---
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id: wiki-2026-0508-data-augmentation
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title: Data Augmentation Strategies
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [data augmentation, AutoAugment, RandAugment, MixUp, CutMix, back translation, Mosaic]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.93
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verification_status: applied
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tags: [data-augmentation, vision, nlp, audio, regularization, autoaugment, mixup, cutmix, generative-augment]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: Python
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framework: torchvision / Albumentations / Augly / nlpaug / Diffusers
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---
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# Data Augmentation
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## 매 한 줄
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> **"매 data 의 양 의 X — 매 모습 의 다양화"**. 매 invariance 의 학습 + 매 overfit 의 방지. 매 vision: rotation, flip, crop, MixUp, CutMix, AutoAugment. 매 NLP: back-translation, 매 LLM-aided. 매 modern: 매 generative augmentation (Stable Diffusion).
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## 매 핵심 strategy
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### Computer Vision
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- **Geometric**: rotation, flip, crop, scale.
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- **Color**: brightness, contrast, hue, saturation.
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- **Noise**: Gaussian, salt-pepper.
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- **MixUp**: 매 two image 의 linear combine.
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- **CutMix**: 매 patch swap.
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- **Cutout**: 매 random masking.
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- **AutoAugment / RandAugment**: 매 learned policy.
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- **TrivialAugment**: 매 random + 매 simple.
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- **Mosaic** (YOLOv5+): 매 4 image 의 grid.
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### NLP
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- **Synonym replacement** (SR).
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- **Random insertion / deletion / swap** (EDA).
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- **Back-translation**: en → fr → en.
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- **Paraphrase**: 매 LLM 의 generate.
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- **Token noise**: 매 BERT-MLM-style.
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- **Mixup-NLP**: 매 hidden representation mix.
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### Audio
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- **Speed / pitch shift**.
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- **SpecAugment**: 매 time + 매 frequency mask.
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- **Noise injection**.
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- **Reverb / EQ**.
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- **Mixup**.
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### Tabular
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- **SMOTE**: 매 minority class 의 synthetic.
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- **Feature noise** (Gaussian).
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- **Mixup-tabular**.
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### Modern (Generative)
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- **Diffusion-based augmentation**: Stable Diffusion 의 generate.
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- **GAN-based**.
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- **LLM-aided text**: 매 paraphrase / extend.
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- **Domain randomization** (sim → real).
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### 매 task-specific
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- **Detection**: bbox-aware augment (Albumentations).
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- **Segmentation**: mask-aware.
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- **Pose**: keypoint-aware.
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- **OCR**: distortion + perspective.
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### 매 modern best practice
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1. **Strong but realistic**: 매 over-augmented X.
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2. **Test-time augmentation** (TTA): 매 inference 의 multiple view.
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3. **AutoML for augmentation**: 매 task-specific policy.
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4. **Curriculum**: 매 weak → strong.
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5. **Domain awareness**: 매 vertical / horizontal flip 의 task 에 따른.
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## 💻 패턴
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### torchvision (vision)
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```python
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import torch
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from torchvision import transforms
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train_transform = transforms.Compose([
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transforms.RandomResizedCrop(224),
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transforms.RandomHorizontalFlip(),
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transforms.ColorJitter(0.4, 0.4, 0.4),
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transforms.RandomRotation(15),
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transforms.RandAugment(num_ops=2, magnitude=9), # 매 modern default
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transforms.ToTensor(),
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transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
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])
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```
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### Albumentations (detection / segmentation)
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```python
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import albumentations as A
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from albumentations.pytorch import ToTensorV2
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transform = A.Compose([
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A.RandomResizedCrop(224, 224, scale=(0.8, 1.0)),
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A.HorizontalFlip(p=0.5),
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A.OneOf([
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A.GaussianBlur(),
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A.MotionBlur(),
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], p=0.3),
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A.RandomBrightnessContrast(p=0.5),
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A.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
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ToTensorV2(),
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], bbox_params=A.BboxParams(format='pascal_voc', label_fields=['class_labels']))
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# 매 bbox + image 의 동시 transform.
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```
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### MixUp (loss-level)
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```python
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def mixup_data(x, y, alpha=0.2):
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lam = np.random.beta(alpha, alpha)
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idx = torch.randperm(x.size(0))
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mixed_x = lam * x + (1 - lam) * x[idx]
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return mixed_x, y, y[idx], lam
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def mixup_loss(criterion, pred, y_a, y_b, lam):
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return lam * criterion(pred, y_a) + (1 - lam) * criterion(pred, y_b)
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# 매 train loop
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for x, y in loader:
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mixed_x, y_a, y_b, lam = mixup_data(x, y)
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pred = model(mixed_x)
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loss = mixup_loss(F.cross_entropy, pred, y_a, y_b, lam)
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loss.backward()
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```
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### CutMix
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```python
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def cutmix(x, y, alpha=1.0):
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lam = np.random.beta(alpha, alpha)
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idx = torch.randperm(x.size(0))
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H, W = x.size(2), x.size(3)
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cut_w = int(W * (1 - lam) ** 0.5)
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cut_h = int(H * (1 - lam) ** 0.5)
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cx, cy = np.random.randint(W), np.random.randint(H)
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x1, y1 = max(cx - cut_w // 2, 0), max(cy - cut_h // 2, 0)
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x2, y2 = min(cx + cut_w // 2, W), min(cy + cut_h // 2, H)
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x[:, :, y1:y2, x1:x2] = x[idx, :, y1:y2, x1:x2]
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lam = 1 - ((x2 - x1) * (y2 - y1) / (W * H))
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return x, y, y[idx], lam
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```
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### NLP — back-translation
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```python
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from transformers import pipeline
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en_to_fr = pipeline('translation', model='Helsinki-NLP/opus-mt-en-fr')
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fr_to_en = pipeline('translation', model='Helsinki-NLP/opus-mt-fr-en')
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def back_translate(text):
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fr = en_to_fr(text, max_length=512)[0]['translation_text']
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return fr_to_en(fr, max_length=512)[0]['translation_text']
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# 매 paraphrase 효과
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augmented = back_translate(original)
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```
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### nlpaug (NLP utility)
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```python
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import nlpaug.augmenter.word as naw
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# 매 contextual (BERT-based) synonym
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aug = naw.ContextualWordEmbsAug(model_path='bert-base-uncased', action='substitute')
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augmented = aug.augment('The quick brown fox jumps')
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```
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### LLM-aided augmentation
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```python
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def llm_paraphrase(text, n=5):
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prompt = f"""Paraphrase the following sentence in {n} different ways while preserving meaning:
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Original: {text}
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Output {n} paraphrases, each on a new line."""
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return llm.generate(prompt).split('\n')
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```
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### Audio (SpecAugment)
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```python
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import torchaudio.transforms as T
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aug = torch.nn.Sequential(
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T.FrequencyMasking(freq_mask_param=30),
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T.TimeMasking(time_mask_param=80),
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)
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mel_spec_augmented = aug(mel_spec)
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```
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### SMOTE (tabular)
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```python
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from imblearn.over_sampling import SMOTE
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smote = SMOTE(random_state=42)
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X_resampled, y_resampled = smote.fit_resample(X, y)
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```
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### Diffusion-based augmentation
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```python
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from diffusers import StableDiffusionImg2ImgPipeline
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pipe = StableDiffusionImg2ImgPipeline.from_pretrained('runwayml/stable-diffusion-v1-5').to('cuda')
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# 매 original image + 매 prompt 의 variation
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augmented = pipe(
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prompt='a {class_name} in different lighting / angle',
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image=original_image,
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strength=0.3, # 매 small change
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num_inference_steps=20,
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).images[0]
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```
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### Test-time augmentation (TTA)
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```python
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def tta_predict(model, image, n=5):
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"""매 매 prediction 의 augment + 매 average."""
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augments = [normal_transform, flip_transform, crop1_transform, ...]
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preds = [model(aug(image)) for aug in augments[:n]]
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return torch.stack(preds).mean(dim=0)
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```
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## 매 결정 기준
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| 상황 | Strategy |
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| Image classification | RandAugment + MixUp |
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| Detection | Albumentations + Mosaic |
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| Segmentation | Mask-aware augment |
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| NLP | Back-translation + LLM paraphrase |
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| Audio | SpecAugment |
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| Imbalanced tabular | SMOTE |
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| Long-tail vision | Class-balanced augment |
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| Generative augment | Diffusion (img2img) |
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**기본값**: RandAugment / TrivialAugment + MixUp/CutMix (vision). LLM paraphrase (NLP).
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## 🔗 Graph
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- 부모: [[Data-Engineering]] · [[L1-and-L2-Regularization|Regularization]]
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- 변형: [[MixUp]] · [[CutMix]] · [[AutoAugment]] · [[Back-Translation]] · [[SMOTE]]
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- Adjacent: [[Bias vs Variance Trade-off]] · [[Cross-Entropy Loss]] · [[CV_Synthesis]] · [[Antifragility]]
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## 🤖 LLM 활용
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**언제**: 매 ML training. 매 small dataset. 매 imbalanced. 매 robustness 필요.
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**언제 X**: 매 already strong model + abundant data.
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## ❌ 안티패턴
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- **Test set 의 augment**: 매 leakage.
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- **Over-augment** (training + test 의 distribute mismatch).
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- **Wrong domain augmentation** (e.g., flipping a "B" → "ⳝ" wrong text).
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- **No bbox-aware** (detection): 매 wrong label.
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- **MixUp 의 label 의 hard target 의 keep**: 매 wrong loss.
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- **Generative augment 의 OOD**: 매 noise.
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## 🧪 검증 / 중복
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- Verified (Cubuk AutoAugment, Zhang MixUp, DeVries CutOut, SpecAugment).
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- 신뢰도 A.
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- Related: [[Bias vs Variance Trade-off]] · [[Cross-Entropy Loss]] · [[CV_Synthesis]] · [[Computer_Vision]] · [[Antifragility]].
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## 🕓 Changelog
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| 날짜 | 변경 |
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — strategy + 매 torchvision / Albumentations / MixUp / back-translate / TTA code |
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