Heuristic Hyperparameter Choice for Image Anomaly Detection

• URL: http://arxiv.org/abs/2307.11197v1
• Date: Thu, 20 Jul 2023 19:20:35 GMT
• Title: Heuristic Hyperparameter Choice for Image Anomaly Detection
• Authors: Zeyu Jiang, Jo\~ao P. C. Bertoldo, Etienne Decenci\`ere
• Abstract summary: Anomaly detection in images is a fundamental computer vision problem by deep learning neural network. Models are usually pretrained on a large dataset for classification tasks such as ImageNet. We aim to do the dimension reduction of Negated Principal Component Analysis (NPCA) for these features.
• Score: 0.3867363075280543
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Anomaly detection (AD) in images is a fundamental computer vision problem by deep learning neural network to identify images deviating significantly from normality. The deep features extracted from pretrained models have been proved to be essential for AD based on multivariate Gaussian distribution analysis. However, since models are usually pretrained on a large dataset for classification tasks such as ImageNet, they might produce lots of redundant features for AD, which increases computational cost and degrades the performance. We aim to do the dimension reduction of Negated Principal Component Analysis (NPCA) for these features. So we proposed some heuristic to choose hyperparameter of NPCA algorithm for getting as fewer components of features as possible while ensuring a good performance.

Related papers

• Chasing Better Deep Image Priors between Over- and Under-parameterization [63.8954152220162]
  We study a novel "lottery image prior" (LIP) by exploiting DNN inherent sparsity. LIPworks significantly outperform deep decoders under comparably compact model sizes. We also extend LIP to compressive sensing image reconstruction, where a pre-trained GAN generator is used as the prior.
  arXiv  Detail & Related papers  (2024-10-31T17:49:44Z)
• Open-Set Deepfake Detection: A Parameter-Efficient Adaptation Method with Forgery Style Mixture [58.60915132222421]
  We introduce an approach that is both general and parameter-efficient for face forgery detection. We design a forgery-style mixture formulation that augments the diversity of forgery source domains. We show that the designed model achieves state-of-the-art generalizability with significantly reduced trainable parameters.
  arXiv  Detail & Related papers  (2024-08-23T01:53:36Z)
• Image anomaly detection and prediction scheme based on SSA optimized ResNet50-BiGRU model [6.95262627755758]
  This paper introduces a network combining Residual Network (ResNet) and Bidirectional Gated Recurrent Unit (BiGRU) It can predict potential injury types and provide early warnings by analyzing changes in muscle and bone poses from video images. Experiments conducted on four datasets demonstrated that our model has the smallest error in image anomaly detection compared to other models.
  arXiv  Detail & Related papers  (2024-06-20T04:26:45Z)
• RIGID: A Training-free and Model-Agnostic Framework for Robust AI-Generated Image Detection [60.960988614701414]
  RIGID is a training-free and model-agnostic method for robust AI-generated image detection. RIGID significantly outperforms existing trainingbased and training-free detectors.
  arXiv  Detail & Related papers  (2024-05-30T14:49:54Z)
• Adapting Visual-Language Models for Generalizable Anomaly Detection in Medical Images [68.42215385041114]
  This paper introduces a novel lightweight multi-level adaptation and comparison framework to repurpose the CLIP model for medical anomaly detection. Our approach integrates multiple residual adapters into the pre-trained visual encoder, enabling a stepwise enhancement of visual features across different levels. Our experiments on medical anomaly detection benchmarks demonstrate that our method significantly surpasses current state-of-the-art models.
  arXiv  Detail & Related papers  (2024-03-19T09:28:19Z)
• Small Object Detection via Coarse-to-fine Proposal Generation and Imitation Learning [52.06176253457522]
  We propose a two-stage framework tailored for small object detection based on the Coarse-to-fine pipeline and Feature Imitation learning. CFINet achieves state-of-the-art performance on the large-scale small object detection benchmarks, SODA-D and SODA-A.
  arXiv  Detail & Related papers  (2023-08-18T13:13:09Z)
• Hyperspectral Remote Sensing Image Classification Based on Multi-scale Cross Graphic Convolution [20.42582692786715]
  New multi-scale feature-mining learning algorithm (MGRNet) is proposed. MGRNet uses principal component analysis to reduce the dimensionality of the original hyperspectral image (HSI) to retain 99.99% of its semantic information. Experiments on three common hyperspectral datasets showed the MGRNet algorithm proposed in this paper to be superior to traditional methods in recognition accuracy.
  arXiv  Detail & Related papers  (2021-06-28T15:28:09Z)
• Anomaly Detection in Image Datasets Using Convolutional Neural Networks, Center Loss, and Mahalanobis Distance [0.0]
  User activities generate a significant number of poor-quality or irrelevant images and data vectors. For neural networks, the anomalous is usually defined as out-of-distribution samples. This work proposes methods for supervised and semi-supervised detection of out-of-distribution samples in image datasets.
  arXiv  Detail & Related papers  (2021-04-13T13:44:03Z)
• Class-Wise Principal Component Analysis for hyperspectral image feature extraction [0.0]
  This paper introduces the Class-wise Principal Component Analysis, a supervised feature extraction method for hyperspectral data. Dimensionality reduction is an essential preprocessing step to complement a hyperspectral image classification task.
  arXiv  Detail & Related papers  (2021-04-09T17:25:11Z)
• Adversarial Feature Augmentation and Normalization for Visual Recognition [109.6834687220478]
  Recent advances in computer vision take advantage of adversarial data augmentation to ameliorate the generalization ability of classification models. Here, we present an effective and efficient alternative that advocates adversarial augmentation on intermediate feature embeddings. We validate the proposed approach across diverse visual recognition tasks with representative backbone networks.
  arXiv  Detail & Related papers  (2021-03-22T20:36:34Z)
• Efficient detection of adversarial images [2.6249027950824506]
  Some or all pixel values of an image are modified by an external attacker, so that the change is almost invisible to the human eye. This paper first proposes a novel pre-processing technique that facilitates the detection of such modified images. An adaptive version of this algorithm is proposed where a random number of perturbations are chosen adaptively.
  arXiv  Detail & Related papers  (2020-07-09T05:35:49Z)

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.