Corpus ID: 166228280

PNUNet: Anomaly Detection using Positive-and-Negative Noise based on Self-Training Procedure

@article{Kimura2019PNUNetAD,
  title={PNUNet: Anomaly Detection using Positive-and-Negative Noise based on Self-Training Procedure},
  author={Masanari Kimura},
  journal={ArXiv},
  year={2019},
  volume={abs/1905.10939}
}
  • Masanari Kimura
  • Published 2019
  • Computer Science, Engineering
  • ArXiv
  • We propose the novel framework for anomaly detection in images. Our new framework, PNUNet, is based on many normal data and few anomalous data. We assume that some noises are added to the input images and learn to remove the noise. In addition, the proposed method achieves significant performance improvement by updating the noise assumed in the inputs using a self-training framework. The experimental results for the benchmark datasets show the usefulness of our new anomaly detection framework. 

    Figures, Tables, and Topics from this paper.

    Explore Further: Topics Discussed in This Paper

    Large-Scale Landslides Detection from Satellite Images with Incomplete Labels

    References

    Publications referenced by this paper.
    SHOWING 1-8 OF 8 REFERENCES
    Efficient GAN-Based Anomaly Detection
    • 153
    • PDF
    Learning Competitive and Discriminative Reconstructions for Anomaly Detection
    • 2
    • PDF
    Semi-supervised Anomaly Detection Using GANs for Visual Inspection in Noisy Training Data
    • 13
    • PDF
    Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery
    • 571
    • PDF
    Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Images
    • 98
    • PDF
    U-Net: Convolutional Networks for Biomedical Image Segmentation
    • 15,500
    • PDF