RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples

• URL: http://arxiv.org/abs/2501.16971v1
• Date: Tue, 28 Jan 2025 14:13:17 GMT
• Title: RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples
• Authors: Hossein Mirzaei, Mohammad Jafari, Hamid Reza Dehbashi, Ali Ansari, Sepehr Ghobadi, Masoud Hadi, Arshia Soltani Moakhar, Mohammad Azizmalayeri, Mahdieh Soleymani Baghshah, Mohammad Hossein Rohban,
• Abstract summary: We introduce RODEO, a data-centric approach that generates effective outliers for robust outlier detection. We show that incorporating outlier exposure (OE) and adversarial training can be an effective strategy for this purpose. We demonstrate both quantitatively and qualitatively that our adaptive OE method effectively generates diverse'' and near-distribution'' outliers.
• Score: 4.76428036044684
• License:
• Abstract: In recent years, there have been significant improvements in various forms of image outlier detection. However, outlier detection performance under adversarial settings lags far behind that in standard settings. This is due to the lack of effective exposure to adversarial scenarios during training, especially on unseen outliers, leading to detection models failing to learn robust features. To bridge this gap, we introduce RODEO, a data-centric approach that generates effective outliers for robust outlier detection. More specifically, we show that incorporating outlier exposure (OE) and adversarial training can be an effective strategy for this purpose, as long as the exposed training outliers meet certain characteristics, including diversity, and both conceptual differentiability and analogy to the inlier samples. We leverage a text-to-image model to achieve this goal. We demonstrate both quantitatively and qualitatively that our adaptive OE method effectively generates ``diverse'' and ``near-distribution'' outliers, leveraging information from both text and image domains. Moreover, our experimental results show that utilizing our synthesized outliers significantly enhances the performance of the outlier detector, particularly in adversarial settings.

Related papers

• Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models [68.90917438865078]
  Deepfake techniques for facial synthesis and editing pose serious risks for generative models. In this paper, we investigate how detection performance varies across model backbones, types, and datasets. We introduce Contrastive Blur, which enhances performance on facial images, and MINDER, which addresses noise type bias, balancing performance across domains.
  arXiv  Detail & Related papers  (2024-11-28T13:04:45Z)
• Regularized Contrastive Partial Multi-view Outlier Detection [76.77036536484114]
  We propose a novel method named Regularized Contrastive Partial Multi-view Outlier Detection (RCPMOD) In this framework, we utilize contrastive learning to learn view-consistent information and distinguish outliers by the degree of consistency. Experimental results on four benchmark datasets demonstrate that our proposed approach could outperform state-of-the-art competitors.
  arXiv  Detail & Related papers  (2024-08-02T14:34:27Z)
• READ: Improving Relation Extraction from an ADversarial Perspective [33.44949503459933]
  We propose an adversarial training method specifically designed for relation extraction (RE) Our approach introduces both sequence- and token-level perturbations to the sample and uses a separate perturbation vocabulary to improve the search for entity and context perturbations.
  arXiv  Detail & Related papers  (2024-04-02T16:42:44Z)
• 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)
• Exploring Robust Features for Improving Adversarial Robustness [11.935612873688122]
  We explore the robust features which are not affected by the adversarial perturbations to improve the model's adversarial robustness. Specifically, we propose a feature disentanglement model to segregate the robust features from non-robust features and domain specific features. The trained domain discriminator is able to identify the domain specific features from the clean images and adversarial examples almost perfectly.
  arXiv  Detail & Related papers  (2023-09-09T00:30:04Z)
• Quantile-based Maximum Likelihood Training for Outlier Detection [5.902139925693801]
  We introduce a quantile-based maximum likelihood objective for learning the inlier distribution to improve the outlier separation during inference. Our approach fits a normalizing flow to pre-trained discriminative features and detects the outliers according to the evaluated log-likelihood.
  arXiv  Detail & Related papers  (2023-08-20T22:27:54Z)
• Enhancing Multiple Reliability Measures via Nuisance-extended Information Bottleneck [77.37409441129995]
  In practical scenarios where training data is limited, many predictive signals in the data can be rather from some biases in data acquisition. We consider an adversarial threat model under a mutual information constraint to cover a wider class of perturbations in training. We propose an autoencoder-based training to implement the objective, as well as practical encoder designs to facilitate the proposed hybrid discriminative-generative training.
  arXiv  Detail & Related papers  (2023-03-24T16:03:21Z)
• R\'enyiCL: Contrastive Representation Learning with Skew R\'enyi Divergence [78.15455360335925]
  We present a new robust contrastive learning scheme, coined R'enyiCL, which can effectively manage harder augmentations. Our method is built upon the variational lower bound of R'enyi divergence. We show that R'enyi contrastive learning objectives perform innate hard negative sampling and easy positive sampling simultaneously.
  arXiv  Detail & Related papers  (2022-08-12T13:37:05Z)
• Homophily Outlier Detection in Non-IID Categorical Data [43.51919113927003]
  This work introduces a novel outlier detection framework and its two instances to identify outliers in categorical data. It first defines and incorporates distribution-sensitive outlier factors and their interdependence into a value-value graph-based representation. The learned value outlierness allows for either direct outlier detection or outlying feature selection.
  arXiv  Detail & Related papers  (2021-03-21T23:29:33Z)
• Negative Data Augmentation [127.28042046152954]
  We show that negative data augmentation samples provide information on the support of the data distribution. We introduce a new GAN training objective where we use NDA as an additional source of synthetic data for the discriminator. Empirically, models trained with our method achieve improved conditional/unconditional image generation along with improved anomaly detection capabilities.
  arXiv  Detail & Related papers  (2021-02-09T20:28:35Z)

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.