Zero-shot Outlier Detection via Prior-data Fitted Networks: Model Selection Bygone!

• URL: http://arxiv.org/abs/2409.05672v1
• Date: Mon, 9 Sep 2024 14:41:24 GMT
• Title: Zero-shot Outlier Detection via Prior-data Fitted Networks: Model Selection Bygone!
• Authors: Yuchen Shen, Haomin Wen, Leman Akoglu,
• Abstract summary: Outlier detection (OD) has numerous applications in environmental monitoring, cybersecurity, finance, and medicine. Being an inherently unsupervised task, model selection is a key bottleneck for OD without label supervision. We present FoMo-0D, for zero/0-shot OD exploring a transformative new direction that bypasses the hurdle of model selection altogether.
• Score: 28.823740273813296
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Outlier detection (OD) has a vast literature as it finds numerous applications in environmental monitoring, cybersecurity, finance, and medicine to name a few. Being an inherently unsupervised task, model selection is a key bottleneck for OD (both algorithm and hyperparameter selection) without label supervision. There is a long list of techniques to choose from -- both classical algorithms and deep neural architectures -- and while several studies report their hyperparameter sensitivity, the literature is quite slim on unsupervised model selection -- limiting the effective use of OD in practice. In this paper we present FoMo-0D, for zero/0-shot OD exploring a transformative new direction that bypasses the hurdle of model selection altogether (!), thus breaking new ground. The fundamental idea behind FoMo-0D is the Prior-data Fitted Networks, recently introduced by Muller et al.(2022), which trains a Transformer model on a large body of synthetically generated data from a prior data distribution. In essence, FoMo-0D is a pretrained Foundation Model for zero/0-shot OD on tabular data, which can directly predict the (outlier/inlier) label of any test data at inference time, by merely a single forward pass -- making obsolete the need for choosing an algorithm/architecture, tuning its associated hyperparameters, and even training any model parameters when given a new OD dataset. Extensive experiments on 57 public benchmark datasets against 26 baseline methods show that FoMo-0D performs statistically no different from the top 2nd baseline, while significantly outperforming the majority of the baselines, with an average inference time of 7.7 ms per test sample.

Related papers

• Out-of-Distribution Detection with a Single Unconditional Diffusion Model [54.15132801131365]
  Out-of-distribution (OOD) detection is a critical task in machine learning that seeks to identify abnormal samples. Traditionally, unsupervised methods utilize a deep generative model for OOD detection. This paper explores whether a single model can perform OOD detection across diverse tasks.
  arXiv  Detail & Related papers  (2024-05-20T08:54:03Z)
• MAPS: A Noise-Robust Progressive Learning Approach for Source-Free Domain Adaptive Keypoint Detection [76.97324120775475]
  Cross-domain keypoint detection methods always require accessing the source data during adaptation. This paper considers source-free domain adaptive keypoint detection, where only the well-trained source model is provided to the target domain.
  arXiv  Detail & Related papers  (2023-02-09T12:06:08Z)
• Toward Unsupervised Outlier Model Selection [20.12322454417006]
  ELECT is a new approach to select an effective model on a new dataset without any labels. It is based on meta-learning; transferring prior knowledge (e.g. model performance) on historical datasets that are similar to the new one. It can serve an output on-demand, being able to accommodate varying time budgets.
  arXiv  Detail & Related papers  (2022-11-03T14:14:46Z)
• Information FOMO: The unhealthy fear of missing out on information. A method for removing misleading data for healthier models [0.0]
  Misleading or unnecessary data can have out-sized impacts on the health or accuracy of Machine Learning (ML) models. We present a sequential selection method that identifies critically important information within a dataset. We find these instabilities are a result of the complexity of the underlying map and linked to extreme events and heavy tails.
  arXiv  Detail & Related papers  (2022-08-27T19:43:53Z)
• Back to the Source: Diffusion-Driven Test-Time Adaptation [77.4229736436935]
  Test-time adaptation harnesses test inputs to improve accuracy of a model trained on source data when tested on shifted target data. We instead update the target data, by projecting all test inputs toward the source domain with a generative diffusion model.
  arXiv  Detail & Related papers  (2022-07-07T17:14:10Z)
• Unsupervised Model Drift Estimation with Batch Normalization Statistics for Dataset Shift Detection and Model Selection [0.0]
  We propose a novel method of model drift estimation by exploiting statistics of batch normalization layer on unlabeled test data. We show the effectiveness of our method not only on dataset shift detection but also on model selection when there are multiple candidate models among model zoo or training trajectories in an unsupervised way.
  arXiv  Detail & Related papers  (2021-07-01T03:04:47Z)
• Time Series Anomaly Detection with label-free Model Selection [0.6303112417588329]
  We propose LaF-AD, a novel anomaly detection algorithm with label-free model selection for unlabeled times-series data. Our algorithm is easily parallelizable, more robust for ill-conditioned and seasonal data, and highly scalable for a large number of anomaly models.
  arXiv  Detail & Related papers  (2021-06-11T00:21:06Z)
• Automating Outlier Detection via Meta-Learning [37.736124230543865]
  We develop the first principled data-driven approach to model selection for outlier detection, called MetaOD, based on meta-learning. We show the effectiveness of MetaOD in selecting a detection model that significantly outperforms the most popular outlier detectors. To foster and further research on this new problem, we open-source our entire meta-learning system, benchmark environment, and testbed datasets.
  arXiv  Detail & Related papers  (2020-09-22T15:14:45Z)
• Contextual-Bandit Anomaly Detection for IoT Data in Distributed Hierarchical Edge Computing [65.78881372074983]
  IoT devices can hardly afford complex deep neural networks (DNN) models, and offloading anomaly detection tasks to the cloud incurs long delay. We propose and build a demo for an adaptive anomaly detection approach for distributed hierarchical edge computing (HEC) systems. We show that our proposed approach significantly reduces detection delay without sacrificing accuracy, as compared to offloading detection tasks to the cloud.
  arXiv  Detail & Related papers  (2020-04-15T06:13:33Z)
• SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier Detection [63.253850875265115]
  Outlier detection (OD) is a key machine learning (ML) task for identifying abnormal objects from general samples. We propose a modular acceleration system, called SUOD, to address it.
  arXiv  Detail & Related papers  (2020-03-11T00:22:50Z)
• Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation [102.67010690592011]
  Unsupervised adaptationUDA (UDA) aims to leverage the knowledge learned from a labeled source dataset to solve similar tasks in a new unlabeled domain. Prior UDA methods typically require to access the source data when learning to adapt the model. This work tackles a practical setting where only a trained source model is available and how we can effectively utilize such a model without source data to solve UDA problems.
  arXiv  Detail & Related papers  (2020-02-20T03:13:58Z)

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.