Optimizing Three Critical Factors for Practical and Effective OOD Detection Fine-Tuning
Updated 7 h ago · first seen 11 Sept 2026
paper_01M294FRN4VDBFTVXB442D4VXK
- Published
- 11 Sept 2026
- T1 · 7 h ago
- arXiv
- 2308.01030
- T1 · 7 h ago
- Category
- cs.LG
- T1 · 7 h ago
Abstract
In out-of-distribution (OOD) detection, fine-tuning with auxiliary outlier data often improves detection performance at the cost of classification accuracy. This trade-off stems from the loss of the original in-distribution (ID) distribution during fine-tuning. To establish a more practical and effective paradigm, we optimize three critical factors: model reminder, data sampling, and representation learning. We propose: (1) Self-Knowledge Distillation (SKD) to mitigate accuracy reduction; (2) Semi-hard Outlier Sampling (SOS) to improve detection efficiency with minimal data; and (3) Outlier-aware Supervised Contrastive Learning (OSCL) to promote ID-OOD separability. Optimizing these factors produces cumulative gains, boosting both OOD detection performance and classification accuracy. Our framework outperforms existing methods across diverse benchmarks, particularly in long-tailed scenarios, providing a robust baseline for real-world OOD detection.
Authors 3
Hyunjun Choi, JaeHo Chung, Hawook Jeong
Specification
- Official page
Source:arXiv (Atom API + RSS)T1observed 7 h agohigh
- Arxiv announce type
- replace
Source:arXiv (Atom API + RSS)T1observed 7 h agohigh
- arXiv id
- 2308.01030
Source:arXiv (Atom API + RSS)T1observed 7 h agohigh
- Categories
- cs.LG, cs.AI, cs.CV
Source:arXiv (Atom API + RSS)T1observed 7 h agohigh
- DOI
- 10.1007/978-3-032-31930-2_29
Source:arXiv (Atom API + RSS)T1observed 7 h agohigh
Source:arXiv (Atom API + RSS)T1observed 7 h agohigh
- Primary category
- cs.LG
Source:arXiv (Atom API + RSS)T1observed 7 h agohigh
- Published
- 11 Sept 2026
Source:arXiv (Atom API + RSS)T1observed 7 h agohigh
Each value shows its source, tier and observation time. Conflicting claims are kept side by side and flagged — never averaged. How AI Atlas records facts →
Provenance
Attributed facts
10
Source tiers
T110
Freshest observation
7 h ago
Conflicts
None
No models linked to this paper yet.
- Authors
- Hyunjun Choi, JaeHo Chung, Hawook Jeong
As of
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Claim history
Official pageofficial_url1
| Value | Valid from → to | Status | Source | Confidence | Extractor |
|---|---|---|---|---|---|
| https://arxiv.org/abs/2308.01030 | → current | current | arXiv (Atom API + RSS)T1 | high | deterministic |
Abstractabstract1
| Value | Valid from → to | Status | Source | Confidence | Extractor |
|---|---|---|---|---|---|
| In out-of-distribution (OOD) detection, fine-tuning with auxiliary outlier data often improves detection performance at the cost of classification accuracy. This trade-off stems from the loss of the original in-distribution (ID) distribution during fine-tuning. To establish a more practical and effective paradigm, we optimize three critical factors: model reminder, data sampling, and representation learning. We propose: (1) Self-Knowledge Distillation (SKD) to mitigate accuracy reduction; (2) Semi-hard Outlier Sampling (SOS) to improve detection efficiency with minimal data; and (3) Outlier-aware Supervised Contrastive Learning (OSCL) to promote ID-OOD separability. Optimizing these factors produces cumulative gains, boosting both OOD detection performance and classification accuracy. Our framework outperforms existing methods across diverse benchmarks, particularly in long-tailed scenarios, providing a robust baseline for real-world OOD detection. | → current | current | arXiv (Atom API + RSS)T1 | high | deterministic |
Arxiv announce typearxiv_announce_type1
| Value | Valid from → to | Status | Source | Confidence | Extractor |
|---|---|---|---|---|---|
| replace | → current | current | arXiv (Atom API + RSS)T1 | high | deterministic |
arXiv idarxiv_id1
| Value | Valid from → to | Status | Source | Confidence | Extractor |
|---|---|---|---|---|---|
| 2308.01030 | → current | current | arXiv (Atom API + RSS)T1 | high | deterministic |
Categoriescategories1
| Value | Valid from → to | Status | Source | Confidence | Extractor |
|---|---|---|---|---|---|
| cs.LG, cs.AI, cs.CV | → current | current | arXiv (Atom API + RSS)T1 | high | deterministic |
DOIdoi1
| Value | Valid from → to | Status | Source | Confidence | Extractor |
|---|---|---|---|---|---|
| 10.1007/978-3-032-31930-2_29 | → current | current | arXiv (Atom API + RSS)T1 | high | deterministic |
PDFpdf_url1
| Value | Valid from → to | Status | Source | Confidence | Extractor |
|---|---|---|---|---|---|
| https://arxiv.org/pdf/2308.01030 | → current | current | arXiv (Atom API + RSS)T1 | high | deterministic |
Primary categoryprimary_category1
| Value | Valid from → to | Status | Source | Confidence | Extractor |
|---|---|---|---|---|---|
| cs.LG | → current | current | arXiv (Atom API + RSS)T1 | high | deterministic |
Publishedpublished_at1
| Value | Valid from → to | Status | Source | Confidence | Extractor |
|---|---|---|---|---|---|
| 11 Sept 2026 | → current | current | arXiv (Atom API + RSS)T1 | high | deterministic |
Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →
- New paperPaperOptimizing Three Critical Factors for Practical and Effective OOD Detection Fine-Tuning
New paper: Optimizing Three Critical Factors for Practical and Effective OOD Detection Fine-Tuning
arxiv
| Source | Document | Type | Tier | Last observed | Snapshots |
|---|---|---|---|---|---|
| arXiv (Atom API + RSS) | rss.arxiv.org/rss/cs.LG | feed | T1· Official | 5 h ago | 1 |
Tier 1 = official/primary, 2 = quality secondary, 3 = community, 4 = unverified. Every snapshot is archived; see all sources and the methodology.