You Only Train Once: Learning a General Anomaly Enhancement Network with Random Masks for Hyperspectral Anomaly Detection

• URL: http://arxiv.org/abs/2303.18001v1
• Date: Fri, 31 Mar 2023 12:23:56 GMT
• Title: You Only Train Once: Learning a General Anomaly Enhancement Network with Random Masks for Hyperspectral Anomaly Detection
• Authors: Zhaoxu Li, Yingqian Wang, Chao Xiao, Qiang Ling, Zaiping Lin, and Wei An
• Abstract summary: We introduce a new approach to address the challenge of generalization in hyperspectral anomaly detection (AD) Our method eliminates the need for adjusting parameters or retraining on new test scenes as required by most existing methods. Our method achieves competitive performance when the training and test set are captured by different sensor devices.
• Score: 31.984085248224574
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we introduce a new approach to address the challenge of generalization in hyperspectral anomaly detection (AD). Our method eliminates the need for adjusting parameters or retraining on new test scenes as required by most existing methods. Employing an image-level training paradigm, we achieve a general anomaly enhancement network for hyperspectral AD that only needs to be trained once. Trained on a set of anomaly-free hyperspectral images with random masks, our network can learn the spatial context characteristics between anomalies and background in an unsupervised way. Additionally, a plug-and-play model selection module is proposed to search for a spatial-spectral transform domain that is more suitable for AD task than the original data. To establish a unified benchmark to comprehensively evaluate our method and existing methods, we develop a large-scale hyperspectral AD dataset (HAD100) that includes 100 real test scenes with diverse anomaly targets. In comparison experiments, we combine our network with a parameter-free detector and achieve the optimal balance between detection accuracy and inference speed among state-of-the-art AD methods. Experimental results also show that our method still achieves competitive performance when the training and test set are captured by different sensor devices. Our code is available at https://github.com/ZhaoxuLi123/AETNet.

Related papers

• Single-temporal Supervised Remote Change Detection for Domain Generalization [42.55492600157288]
  Change detection is widely applied in remote sensing image analysis. Existing methods require training models separately for each dataset. We propose a multimodal contrastive learning (ChangeCLIP) based on visual-labelled pre-training for change detection domain generalization.
  arXiv  Detail & Related papers  (2024-04-17T12:38:58Z)
• Learning Feature Inversion for Multi-class Anomaly Detection under General-purpose COCO-AD Benchmark [101.23684938489413]
  Anomaly detection (AD) is often focused on detecting anomalies for industrial quality inspection and medical lesion examination. This work first constructs a large-scale and general-purpose COCO-AD dataset by extending COCO to the AD field. Inspired by the metrics in the segmentation field, we propose several more practical threshold-dependent AD-specific metrics.
  arXiv  Detail & Related papers  (2024-04-16T17:38:26Z)
• COFT-AD: COntrastive Fine-Tuning for Few-Shot Anomaly Detection [19.946344683965425]
  We propose a novel methodology to address the challenge of FSAD. We employ a model pre-trained on a large source dataset to model weights. We evaluate few-shot anomaly detection on on 3 controlled AD tasks and 4 real-world AD tasks to demonstrate the effectiveness of the proposed method.
  arXiv  Detail & Related papers  (2024-02-29T09:48:19Z)
• LafitE: Latent Diffusion Model with Feature Editing for Unsupervised Multi-class Anomaly Detection [12.596635603629725]
  We develop a unified model to detect anomalies from objects belonging to multiple classes when only normal data is accessible. We first explore the generative-based approach and investigate latent diffusion models for reconstruction. We introduce a feature editing strategy that modifies the input feature space of the diffusion model to further alleviate identity shortcuts''
  arXiv  Detail & Related papers  (2023-07-16T14:41:22Z)
• Generalizable Metric Network for Cross-domain Person Re-identification [55.71632958027289]
  Cross-domain (i.e., domain generalization) scene presents a challenge in Re-ID tasks. Most existing methods aim to learn domain-invariant or robust features for all domains. We propose a Generalizable Metric Network (GMN) to explore sample similarity in the sample-pair space.
  arXiv  Detail & Related papers  (2023-06-21T03:05:25Z)
• Dynamic Prototype Mask for Occluded Person Re-Identification [88.7782299372656]
  Existing methods mainly address this issue by employing body clues provided by an extra network to distinguish the visible part. We propose a novel Dynamic Prototype Mask (DPM) based on two self-evident prior knowledge. Under this condition, the occluded representation could be well aligned in a selected subspace spontaneously.
  arXiv  Detail & Related papers  (2022-07-19T03:31:13Z)
• Self-Supervision & Meta-Learning for One-Shot Unsupervised Cross-Domain Detection [0.0]
  We present an object detection algorithm able to perform unsupervised adaptation across domains by using only one target sample, seen at test time. We exploit meta-learning to simulate single-sample cross domain learning episodes and better align to the test condition.
  arXiv  Detail & Related papers  (2021-06-07T10:33:04Z)
• DAAIN: Detection of Anomalous and Adversarial Input using Normalizing Flows [52.31831255787147]
  We introduce a novel technique, DAAIN, to detect out-of-distribution (OOD) inputs and adversarial attacks (AA) Our approach monitors the inner workings of a neural network and learns a density estimator of the activation distribution. Our model can be trained on a single GPU making it compute efficient and deployable without requiring specialized accelerators.
  arXiv  Detail & Related papers  (2021-05-30T22:07:13Z)
• Meta-learning One-class Classifiers with Eigenvalue Solvers for Supervised Anomaly Detection [55.888835686183995]
  We propose a neural network-based meta-learning method for supervised anomaly detection. We experimentally demonstrate that the proposed method achieves better performance than existing anomaly detection and few-shot learning methods.
  arXiv  Detail & Related papers  (2021-03-01T01:43:04Z)
• 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)

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.