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Antigravity Agent
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id: wiki-2026-0508-out-of-distribution-detection
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title: Out-of-Distribution Detection
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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: [OOD-Detection, OOD, Anomaly-Detection-NN, Novelty-Detection]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.9
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verification_status: applied
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tags: [ood, safety, uncertainty, foundation-models, anomaly-detection]
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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: pytorch
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---
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# Out-of-Distribution Detection
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## 매 한 줄
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> **"매 model 이 본 적 없는 input 의 거부"**. OOD detection 은 inference 시 input 이 training distribution 밖인지 판정하여 silent failure 를 막는 safety layer. 매 2026 의 표준은 foundation-model embedding 위 의 KNN / Mahalanobis 또는 logit-energy score, classical ODIN 은 baseline.
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## 매 핵심
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### 매 score family
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1. **Softmax baseline (MSP)**: max softmax probability — weak baseline.
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2. **ODIN** (Liang 2018): temperature scaling + input gradient perturbation.
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3. **Energy** (Liu 2020): `-T * logsumexp(logits / T)`, free, strong.
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4. **Mahalanobis** (Lee 2018): class-conditional Gaussian on penultimate features.
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5. **KNN** (Sun 2022): k-NN distance in feature space — 매 simple, robust.
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6. **DOSE / VIM** (2022-2024): residual + logit hybrid.
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7. **Foundation-model OOD** (CLIP, DINOv2 features + KNN) — 2026 SOTA.
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### 매 evaluation
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- AUROC, FPR@95TPR, AUPR.
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- ID = CIFAR-10/ImageNet, OOD = SVHN, Textures, iNaturalist, Places, OpenOOD bench.
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- **near-OOD** (CIFAR10 vs CIFAR100) 가 매 어려운 case.
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### 매 응용
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1. autonomous driving 의 unknown object reject.
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2. medical imaging 의 unsupported modality flag.
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3. LLM 의 jailbreak / off-distribution prompt detection.
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4. fraud detection 의 novel attack pattern.
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## 💻 패턴
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### Energy score (Liu 2020)
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```python
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import torch, torch.nn.functional as F
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@torch.no_grad()
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def energy_score(model, x, T=1.0):
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logits = model(x)
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# higher energy = OOD
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return -T * torch.logsumexp(logits / T, dim=-1)
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```
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### MSP baseline
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```python
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@torch.no_grad()
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def msp(model, x):
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return -F.softmax(model(x), dim=-1).max(-1).values
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```
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### Mahalanobis on features
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```python
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@torch.no_grad()
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def fit_mahalanobis(features, labels, num_classes):
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means = []
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for c in range(num_classes):
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means.append(features[labels == c].mean(0))
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means = torch.stack(means)
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centered = features - means[labels]
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cov = centered.T @ centered / len(features)
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inv = torch.linalg.pinv(cov)
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return means, inv
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def maha_score(f, means, inv):
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diffs = f.unsqueeze(1) - means # [N, C, D]
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d2 = torch.einsum("ncd,de,nce->nc", diffs, inv, diffs)
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return d2.min(-1).values # smallest distance to any class
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```
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### KNN OOD (Sun 2022)
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```python
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import torch, torch.nn.functional as F
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class KNNOOD:
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def __init__(self, k=50):
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self.k = k
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def fit(self, train_feats):
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self.bank = F.normalize(train_feats, dim=-1)
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def score(self, feats):
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f = F.normalize(feats, dim=-1)
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sim = f @ self.bank.T # cosine
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topk = sim.topk(self.k, dim=-1).values
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return -topk[:, -1] # negative k-th similarity → OOD score
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```
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### ODIN
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```python
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def odin_score(model, x, T=1000, eps=0.0014):
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x = x.clone().detach().requires_grad_(True)
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logits = model(x) / T
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p = F.softmax(logits, dim=-1).max(-1).values
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p.sum().backward()
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x_adv = x - eps * x.grad.sign()
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with torch.no_grad():
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return F.softmax(model(x_adv) / T, dim=-1).max(-1).values
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```
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### Foundation-model OOD (DINOv2 + KNN)
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```python
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import torch
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dino = torch.hub.load("facebookresearch/dinov2", "dinov2_vitb14").eval().cuda()
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@torch.no_grad()
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def feats(x):
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return dino(x) # [B, 768]
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knn = KNNOOD(k=50)
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knn.fit(feats(train_loader_id))
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ood_scores = knn.score(feats(test_batch))
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```
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### LLM OOD via embedding
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```python
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from sentence_transformers import SentenceTransformer
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emb = SentenceTransformer("BAAI/bge-large-en-v1.5")
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id_bank = emb.encode(in_dist_prompts, normalize_embeddings=True)
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def prompt_ood(prompt, k=20):
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q = emb.encode([prompt], normalize_embeddings=True)
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sims = (q @ id_bank.T)[0]
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return -sims.topk(k).values.min()
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```
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### Threshold calibration (FPR@95TPR)
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```python
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import numpy as np
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def threshold_at_tpr(scores_id, tpr=0.95):
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return np.quantile(scores_id, 1 - tpr)
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```
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## 매 결정 기준
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| 상황 | Method |
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|---|---|
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| 매 simple, 즉시 | Energy |
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| 매 best AUROC | KNN on foundation features |
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| 매 access to features only | Mahalanobis |
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| 매 CV with strong backbone | DINOv2 + KNN |
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| 매 LLM input filter | embedding KNN + threshold |
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| 매 production, low-latency | Energy or MSP |
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**기본값**: foundation embedding + KNN (k=50).
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## 🔗 Graph
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- 부모: [[Anomaly-Detection]]
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- 변형: [[KNN]]
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- 응용: [[Active Learning]]
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## 🤖 LLM 활용
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**언제**: 매 high-stakes deployment, jailbreak filter, novel-prompt routing.
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**언제 X**: 매 closed-world benchmark — distribution 가 fixed 인 경우 overhead.
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## ❌ 안티패턴
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- **MSP only**: 매 over-confident network 에서 거의 무력.
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- **Train OOD detector on test OOD set**: leakage, false confidence.
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- **Threshold from training scores**: ID validation set 에서 calibrate.
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- **Ignore near-OOD**: far-OOD AUROC 99% 인데 near-OOD 60% 인 흔한 함정.
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- **Foundation-model embedding mismatch**: ImageNet-pretrained 으로 medical OOD detect.
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## 🧪 검증 / 중복
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- Verified (OpenOOD benchmark 2024, Sun 2022 KNN, Liu 2020 Energy).
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- 신뢰도 A.
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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 — Energy/Maha/KNN + foundation-model OOD |
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