DOS: Diverse Outlier Sampling for Out-of-Distribution Detection

• URL: http://arxiv.org/abs/2306.02031v2
• Date: Sun, 25 Feb 2024 06:59:50 GMT
• Title: DOS: Diverse Outlier Sampling for Out-of-Distribution Detection
• Authors: Wenyu Jiang, Hao Cheng, Mingcai Chen, Chongjun Wang, Hongxin Wei
• Abstract summary: We show that diversity is critical in sampling outliers for OOD detection performance. We propose a straightforward and novel sampling strategy named DOS (Diverse Outlier Sampling) to select diverse and informative outliers.
• Score: 18.964462007139055
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modern neural networks are known to give overconfident prediction for out-of-distribution inputs when deployed in the open world. It is common practice to leverage a surrogate outlier dataset to regularize the model during training, and recent studies emphasize the role of uncertainty in designing the sampling strategy for outlier dataset. However, the OOD samples selected solely based on predictive uncertainty can be biased towards certain types, which may fail to capture the full outlier distribution. In this work, we empirically show that diversity is critical in sampling outliers for OOD detection performance. Motivated by the observation, we propose a straightforward and novel sampling strategy named DOS (Diverse Outlier Sampling) to select diverse and informative outliers. Specifically, we cluster the normalized features at each iteration, and the most informative outlier from each cluster is selected for model training with absent category loss. With DOS, the sampled outliers efficiently shape a globally compact decision boundary between ID and OOD data. Extensive experiments demonstrate the superiority of DOS, reducing the average FPR95 by up to 25.79% on CIFAR-100 with TI-300K.

Related papers

• Debiased Sample Selection for Combating Noisy Labels [24.296451733127956]
  We propose a noIse-Tolerant Expert Model (ITEM) for debiased learning in sample selection. Specifically, to mitigate the training bias, we design a robust network architecture that integrates with multiple experts. By training on the mixture of two class-discriminative mini-batches, the model mitigates the effect of the imbalanced training set.
  arXiv  Detail & Related papers  (2024-01-24T10:37:28Z)
• Spanning Training Progress: Temporal Dual-Depth Scoring (TDDS) for Enhanced Dataset Pruning [50.809769498312434]
  We propose a novel dataset pruning method termed as Temporal Dual-Depth Scoring (TDDS) Our method achieves 54.51% accuracy with only 10% training data, surpassing random selection by 7.83% and other comparison methods by at least 12.69%.
  arXiv  Detail & Related papers  (2023-11-22T03:45:30Z)
• Out-of-distribution Object Detection through Bayesian Uncertainty Estimation [10.985423935142832]
  We propose a novel, intuitive, and scalable probabilistic object detection method for OOD detection. Our method is able to distinguish between in-distribution (ID) data and OOD data via weight parameter sampling from proposed Gaussian distributions. We demonstrate that our Bayesian object detector can achieve satisfactory OOD identification performance by reducing the FPR95 score by up to 8.19% and increasing the AUROC score by up to 13.94% when trained on BDD100k and VOC datasets.
  arXiv  Detail & Related papers  (2023-10-29T19:10:52Z)
• Diversified Outlier Exposure for Out-of-Distribution Detection via Informative Extrapolation [110.34982764201689]
  Out-of-distribution (OOD) detection is important for deploying reliable machine learning models on real-world applications. Recent advances in outlier exposure have shown promising results on OOD detection via fine-tuning model with informatively sampled auxiliary outliers. We propose a novel framework, namely, Diversified Outlier Exposure (DivOE), for effective OOD detection via informative extrapolation based on the given auxiliary outliers.
  arXiv  Detail & Related papers  (2023-10-21T07:16:09Z)
• Selecting Learnable Training Samples is All DETRs Need in Crowded Pedestrian Detection [72.97320260601347]
  In crowded pedestrian detection, the performance of DETRs is still unsatisfactory due to the inappropriate sample selection method. We propose Sample Selection for Crowded Pedestrians, which consists of the constraint-guided label assignment scheme (CGLA) Experimental results show that the proposed SSCP effectively improves the baselines without introducing any overhead in inference.
  arXiv  Detail & Related papers  (2023-05-18T08:28:01Z)
• Robust Outlier Rejection for 3D Registration with Variational Bayes [70.98659381852787]
  We develop a novel variational non-local network-based outlier rejection framework for robust alignment. We propose a voting-based inlier searching strategy to cluster the high-quality hypothetical inliers for transformation estimation.
  arXiv  Detail & Related papers  (2023-04-04T03:48:56Z)
• Delving into Identify-Emphasize Paradigm for Combating Unknown Bias [52.76758938921129]
  We propose an effective bias-conflicting scoring method (ECS) to boost the identification accuracy. We also propose gradient alignment (GA) to balance the contributions of the mined bias-aligned and bias-conflicting samples. Experiments are conducted on multiple datasets in various settings, demonstrating that the proposed solution can mitigate the impact of unknown biases.
  arXiv  Detail & Related papers  (2023-02-22T14:50:24Z)
• ScatterSample: Diversified Label Sampling for Data Efficient Graph Neural Network Learning [22.278779277115234]
  In some applications where graph neural network (GNN) training is expensive, labeling new instances is expensive. We develop a data-efficient active sampling framework, ScatterSample, to train GNNs under an active learning setting. Our experiments on five datasets show that ScatterSample significantly outperforms the other GNN active learning baselines.
  arXiv  Detail & Related papers  (2022-06-09T04:05:02Z)
• One for More: Selecting Generalizable Samples for Generalizable ReID Model [92.40951770273972]
  This paper proposes a one-for-more training objective that takes the generalization ability of selected samples as a loss function. Our proposed one-for-more based sampler can be seamlessly integrated into the ReID training framework.
  arXiv  Detail & Related papers  (2020-12-10T06:37:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.