arXiv Machine Learning
Sep 11

Optimizing Three Critical Factors for Practical and Effective OOD Detection Fine-Tuning

The paper introduces a framework for out-of-distribution (OOD) detection that addresses the trade‑off between detection performance and classification accuracy caused by fine‑tuning with auxiliary outlier data. It optimizes three factors—model reminder, data sampling, and representation learning—by proposing Self‑Knowledge Distillation to preserve accuracy, Semi‑hard Outlier Sampling to enhance detection with minimal data, and Outlier‑aware Supervised Contrastive Learning to improve ID‑OOD separability. The combined approach yields cumulative gains, outperforming existing methods on diverse benchmarks, especially in long‑tailed scenarios, and offers a robust baseline for real‑world OOD detection.

By Hyunjun Choi, JaeHo Chung, Hawook Jeong
arXiv Machine Learning
Jun 10

XtrAIn: Training-Guided Occlusion for Feature Attribution

arXiv:2606. 10877v1 Announce Type: new Abstract: Occlusion-based attribution methods provide an intuitive way to estimate feature importance by perturbing input features and measuring the resulting change in model output.

By Thodoris Lymperopoulos, Ioannis Kakogeorgiou, Denia Kanellopoulou
arXiv Machine Learning
Jul 21

AOE: Exhaustive Out-of-Distribution Detection via Recalibrating Outlier Labels

arXiv:2605. 28021v2 Announce Type: replace Abstract: Out-of-distribution (OOD) detection is essential for deploying machine learning models in open-world and safety-critical scenarios, where test inputs may deviate from the training distribution and overconfident predictions on unknown samples can lead to unreliable decisions.

By Fengqiang Wan, Qing-Yuan Jiang, Fu Shen, Yang Yang
arXiv Machine Learning
Jun 19

Model soups need only one ingredient

arXiv:2602. 09689v2 Announce Type: replace Abstract: Fine-tuning large pre-trained models on a target distribution often improves in-distribution (ID) accuracy, but at the cost of out-of-distribution (OOD) robustness as representations specialize to the fine-tuning data.

By Alireza Abdollahpoorrostam, Nikolaos Dimitriadis, Adam Hazimeh, Pascal Frossard
arXiv Statistics ML
6d ago

Prior Predictive Matching for Bayesian LoRA

arXiv:2610.35457v1 Announce Type: cross Abstract: Bayesian variants of Low-Rank Adaptation (LoRA) provide principled uncertainty estimation when fine-tuning large foundation models, but require speci...

By Tomasz Ku\'smierczyk, Eliezer de Souza da Silva, Marcin Sendera