refactor(topics): 멀티 에이전트용 지식 재편 — _Common(공통 기본기) + Domain_* 구조

에이전트 8종(대화형/프로그래머 C·S/디자이너/설계자/기획자/QA/PD/PM)에게
[공통 기본 능력 + 롤별 Specialty] 2층으로 지식을 주입하기 위한 재분류.
문서 내용·포맷은 무수정, 폴더 이동만 (6,372개 문서 수 보존 확인).

- Topic_Programming → Domain_Programming (내부 구조 보존)
- Topic_Graphic → Domain_Design
- Topic_Business → Domain_Product
- Topic_General → Domain_General
- _Common 신설: Math(구 Topic_Math_Specialty), Reasoning(구 General/From_Thinking & Reasoning),
  Reasoning_Creativity(구 General/From_창의성), Communication(Poetic_Blog_Writing + From_writing)
- 타 도메인의 From_* 폴더는 유지 (출처 표기일 뿐, 이미 도메인에 맞게 분류된 문서)
- 빈 폴더 정리 (memory/procedures)
- 에이전트→폴더 매핑은 workspace의 .astra/agent-knowledge-map.json (9개 에이전트)

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

10_Wiki/Topics/Domain_Programming/AI_and_ML/Loss-Functions-Foundations.md

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+---
+id: wiki-2026-0508-loss-functions-foundations
+title: Loss Functions Foundations
+category: 10_Wiki/Topics
+status: verified
+canonical_id: self
+aliases: [Loss Functions, Cost Functions, Objective Functions, Loss-Functions]
+duplicate_of: none
+source_trust_level: A
+confidence_score: 0.95
+verification_status: applied
+tags: [loss, objective, training, mse, cross-entropy, focal, contrastive, dice]
+raw_sources: []
+last_reinforced: 2026-05-10
+github_commit: pending
+tech_stack: { language: Python, framework: PyTorch }
+---
+
+# Loss Functions Foundations
+
+## 매 한 줄
+> **"매 loss는 task가 정한다"**. Regression→MSE/MAE/Huber, Classification→CE/Focal, Metric→Contrastive/Triplet, Segmentation→Dice/IoU.
+
+## 매 핵심
+### 매 회귀 (Regression)
+- **MSE (L2)**: ½(y-ŷ)². 미분 깔끔, outlier에 민감.
+- **MAE (L1)**: |y-ŷ|. robust, 0에서 미분 불가.
+- **Huber**: |e|<δ면 MSE, 아니면 MAE. δ=1 기본.
+- **Log-cosh**: smooth Huber. 자동 미분 친화.
+- **Quantile**: max(τe, (τ-1)e). 중앙값/구간 예측.
+
+### 매 분류 (Classification)
+- **BCE**: -[y log p + (1-y) log(1-p)]. 이진/다중라벨.
+- **CE (softmax)**: -Σ y_k log p_k. 다중클래스.
+- **Focal** (Lin 2017): -α(1-p)^γ log p. easy example down-weight, γ=2 기본.
+- **Label smoothing**: y → y(1-ε) + ε/K. overconfidence 방지.
+- **Hinge**: max(0, 1-y·ŷ). SVM. y∈{-1,+1}.
+
+### 매 Metric Learning
+- **Contrastive** (Hadsell 2006): pair. y·d² + (1-y)·max(0, m-d)².
+- **Triplet**: max(0, d(a,p) - d(a,n) + margin).
+- **InfoNCE / NT-Xent** (SimCLR): -log exp(sim+/τ) / Σ exp(sim/τ).
+- **Cosine embedding**: 1 - cos(a,b).
+
+### 매 Segmentation
+- **Dice**: 1 - 2|A∩B|/(|A|+|B|). class imbalance 강함.
+- **IoU/Jaccard**: 1 - |A∩B|/|A∪B|.
+- **Tversky**: FP/FN weight 조정.
+- **Boundary loss**: 거리변환 가중.
+
+## 💻 패턴
+### Regression losses
+```python
+import torch, torch.nn.functional as F
+mse = F.mse_loss(pred, y)
+mae = F.l1_loss(pred, y)
+huber = F.huber_loss(pred, y, delta=1.0)        # smooth_l1 ≈ huber(δ=1)
+# Quantile
+def quantile_loss(pred, y, tau=0.5):
+    e = y - pred
+    return torch.maximum(tau*e, (tau-1)*e).mean()
+```
+
+### Classification losses
+```python
+ce = F.cross_entropy(logits, y_int)               # logits, not probs
+bce = F.binary_cross_entropy_with_logits(logits, y_float)
+# Label smoothing (built-in)
+ce_ls = F.cross_entropy(logits, y_int, label_smoothing=0.1)
+```
+
+### Focal loss (이진)
+```python
+def focal_bce(logits, y, alpha=0.25, gamma=2.0):
+    p = torch.sigmoid(logits)
+    pt = torch.where(y == 1, p, 1 - p)
+    alpha_t = torch.where(y == 1, alpha, 1 - alpha)
+    return -(alpha_t * (1 - pt).pow(gamma) * pt.clamp_min(1e-8).log()).mean()
+```
+
+### Triplet & InfoNCE
+```python
+triplet = F.triplet_margin_loss(anchor, pos, neg, margin=1.0)
+
+def info_nce(q, k_pos, k_neg, tau=0.07):
+    # q: (B,D), k_pos: (B,D), k_neg: (B,N,D)
+    pos = (q * k_pos).sum(-1, keepdim=True) / tau
+    neg = torch.einsum("bd,bnd->bn", q, k_neg) / tau
+    logits = torch.cat([pos, neg], dim=1)
+    target = torch.zeros(q.size(0), dtype=torch.long, device=q.device)
+    return F.cross_entropy(logits, target)
+```
+
+### Dice + BCE (segmentation 표준)
+```python
+def dice_loss(logits, y, eps=1e-6):
+    p = torch.sigmoid(logits)
+    inter = (p * y).sum(dim=(2, 3))
+    union = p.sum(dim=(2, 3)) + y.sum(dim=(2, 3))
+    return 1 - (2 * inter + eps) / (union + eps)
+
+def combo_loss(logits, y):
+    return 0.5 * F.binary_cross_entropy_with_logits(logits, y) + dice_loss(logits, y).mean()
+```
+
+### Class imbalance 가중
+```python
+weights = torch.tensor([1.0, 5.0, 2.0])           # 클래스별
+ce_w = F.cross_entropy(logits, y_int, weight=weights)
+```
+
+## 매 결정 기준
+| Task | Default | 변형 |
+|---|---|---|
+| Regression normal | MSE | outlier→Huber, robust→MAE |
+| Binary classification | BCE w/ logits | imbalance→Focal |
+| Multi-class | CE w/ label smoothing | imbalance→class weights |
+| Multi-label | BCE per-class |  |
+| Embedding learning | InfoNCE | small batch→Triplet |
+| Segmentation | BCE+Dice | small object→Tversky |
+| Object detection | Focal + IoU/GIoU | (RetinaNet, YOLO) |
+
+**기본값**: classification CE+label smoothing 0.1, regression Huber.
+
+## 🔗 Graph
+- 부모: [[Optimization]]
+- 변형: [[Focal-Loss]]
+- 응용: [[Image-Classification-Mastery]], [[Segmentation]], [[Object-Detection]]
+- Adjacent: [[Activation-Functions]], [[Class-Imbalance]], [[L1-and-L2-Regularization|Regularization]]
+
+## 🤖 LLM 활용
+**언제**: task→loss 매핑, gradient 직관, 코드 템플릿 생성.
+**언제 X**: domain-specific custom loss 설계는 검증 필수 (분포·gradient 분석).
+
+## ❌ 안티패턴
+- `softmax` 후 `nll_loss` 손수 (numerical) ← `cross_entropy` 사용
+- BCE에 `binary_cross_entropy(sigmoid(...))` ← `_with_logits` 사용
+- Imbalance 무시한 CE
+- Dice loss만 단독 (gradient 불안정) → BCE+Dice 혼합
+- Focal γ를 imbalance 없을 때 사용 (성능↓)
+
+## 🧪 검증 / 중복
+- Verified (Goodfellow DL ch5, Lin 2017 Focal, SimCLR, Milletari V-Net Dice). 신뢰도 A.
+- Canonical for [[Loss-Functions-Foundations|Loss Functions]] (redirect).
+
+## 🕓 Changelog
+| 날짜 | 변경 |
+|---|---|
+| 2026-05-08 | Phase 1 |
+| 2026-05-10 | Manual cleanup — canonical 강화, segmentation/metric 추가 |