mlsanityData to 14 Oct 2026

Deep Metric Learning-Based Out-of-Distribution Detection with Synthetic Outlier Exposure

arXiv 2405.00631 ↗First posted 1 May 2024cs.CVcited by 0

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

In this paper, we present a novel approach that combines deep metric learning and synthetic data generation using diffusion models for out-of-distribution (OOD) detection. One popular approach for OOD detection is outlier exposure, where models are trained using a mixture of in-distribution (ID) samples and ``seen" OOD samples. For the OOD samples, the model is trained to minimize the KL divergence between the output probability and the uniform distribution while correctly classifying the in-distribution (ID) data. In this paper, we propose a label-mixup approach to generate synthetic OOD data using Denoising Diffusion Probabilistic Models (DDPMs). Additionally, we explore recent advancements in metric learning to train our models. In the experiments, we found that metric learning-based loss functions perform better than the softmax. Furthermore, the baseline models (including softmax, and metric learning) show a significant improvement when trained with the generated OOD data. Our approach outperforms strong baselines in conventional OOD detection metrics.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 28 of its 41 references that are in the map. First posted on arXiv 1 May 2024.

Builds on

28 of 41 references.

Closest ancestors · 2

Same direction, newest first

  1. 2023Unsupervised Out-of-Distribution Detection with Diffusion Inpainting20 Feb
  2. 2022Denoising diffusion models for out-of-distribution detection14 Nov

Other references · 26

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  3. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,183
  4. 2016A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networkscited by 2,385
  5. 2018Unsupervised Feature Learning via Non-Parametric Instance-level Discriminationcited by 1,845
  6. 2018A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attackscited by 1,485
  7. 2015LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loopcited by 1,355
  8. 2020Energy-based Out-of-distribution Detectioncited by 1,299
  9. 2017Learning to Compare: Relation Network for Few-Shot Learningcited by 1,143
  10. 2018Deep Clustering for Unsupervised Learning of Visual Featurescited by 1,028
  11. 2018CosFace: Large Margin Cosine Loss for Deep Face Recognitioncited by 971
  12. 2018Deep Anomaly Detection with Outlier Exposurecited by 942
14 more
  1. 2017SphereFace: Deep Hypersphere Embedding for Face Recognitioncited by 737
  2. 2022Out-of-Distribution Detection with Deep Nearest Neighborscited by 600
  3. 2020CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instancescited by 441
  4. 2017Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samplescited by 389
  5. 2022VOS: Learning What You Don't Know by Virtual Outlier Synthesiscited by 300
  6. 2015TurkerGaze: Crowdsourcing Saliency with Webcam based Eye Trackingcited by 165
  7. 2021Semantically Coherent Out-of-Distribution Detectioncited by 107
  8. 2022Training OOD Detectors in their Natural Habitatscited by 86
  9. 2019AdaCos: Adaptively Scaling Cosine Logits for Effectively Learning Deep Face Representationscited by 83
  10. 2020DPGN: Distribution Propagation Graph Network for Few-shot Learningcited by 79
  11. 2019Out-of-distribution Detection in Classifiers via Generationcited by 49
  12. 2020Rethinking Class Relations: Absolute-relative Supervised and Unsupervised Few-shot Learningcited by 31
  13. 2020BSNet: Bi-Similarity Network for Few-shot Fine-grained Image Classificationcited by 30
  14. 2022CADet: Fully Self-Supervised Out-Of-Distribution Detection With Contrastive Learningcited by 11

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  1. 2024Enhancing OOD Detection Using Latent Diffusion
  2. 2024Exploiting Diffusion Prior for Out-of-Distribution Detection
  3. 2024Non-Linear Outlier Synthesis for Out-of-Distribution Detection
  4. 2025Dream-Box: Object-wise Outlier Generation for Out-of-Distribution Detection
  5. 2025Beyond Binary Out-of-Distribution Detection: Characterizing Distributional Shifts with Multi-Statistic Diffusion Trajectories
  6. 2024Out-of-Distribution Detection with a Single Unconditional Diffusion Model
  7. 2022Diffusion Denoising Process for Perceptron Bias in Out-of-distribution Detection
  8. 2024NODI: Out-Of-Distribution Detection with Noise from Diffusion