Search Results for author:
Found 18 papers, 3 papers with code
Scene Depth Estimation from Traditional Oriental Landscape Paintings
To address the problem of scene depth estimation from oriental landscape painting images, we propose a novel framework that consists of two-step Image-to-Image translation method with CLIP-based image matching at the front end to predict the real scene image that best matches with the given oriental landscape painting image.
Empirical Analysis of Anomaly Detection on Hyperspectral Imaging Using Dimension Reduction Methods
Recent studies try to use hyperspectral imaging (HSI) to detect foreign matters in products because it enables to visualize the invisible wavelengths including ultraviolet and infrared.
Excision And Recovery: Visual Defect Obfuscation Based Self-Supervised Anomaly Detection Strategy
To enhance the UAD performance, reconstruction-by-inpainting based methods have recently been investigated, especially on the masking strategy of suspected defective regions.
Ranked #66 on
Anomaly Detection
on MVTec AD
Self-Supervised Anomaly Detection
Supervised Anomaly Detection
+1
Neural Network Training Strategy to Enhance Anomaly Detection Performance: A Perspective on Reconstruction Loss Amplification
In contrast, we note that containing of generalization ability in reconstruction can also be obtained simply from steep-shaped loss landscape.
Edge Storage Management Recipe with Zero-Shot Data Compression for Road Anomaly Detection
However, the edge computers will have small data storage but we need to store the collected audio samples for a long time in order to update existing models or develop a novel method.
Frequency of Interest-based Noise Attenuation Method to Improve Anomaly Detection Performance
Accurately extracting driving events is the way to maximize computational efficiency and anomaly detection performance in the tire frictional nose-based anomaly detection task.
Concise Logarithmic Loss Function for Robust Training of Anomaly Detection Model
For the training anomaly detection model, the mean squared error (MSE) function is adopted widely.
Latent Vector Expansion using Autoencoder for Anomaly Detection
We train normal and abnormal data as a feature that has a strong distinction among the features of imbalanced data.
Noise Reduction and Driving Event Extraction Method for Performance Improvement on Driving Noise-based Surface Anomaly Detection
Foreign substances on the road surface, such as rainwater or black ice, reduce the friction between the tire and the surface.
Efficient Non-Compression Auto-Encoder for Driving Noise-based Road Surface Anomaly Detection
As a result, the computational cost of the neural network is reduced up to 1 over 25 compared to the conventional models and the anomaly detection performance is improved by up to 7. 72%.
Anomaly Detection Based on Multiple-Hypothesis Autoencoder
A model trained with normal data generates a larger restoration error for abnormal data.
Self-Weighted Ensemble Method to Adjust the Influence of Individual Models based on Reliability
Image classification technology and performance based on Deep Learning have already achieved high standards.
Non-Compression Auto-Encoder for Detecting Road Surface Abnormality via Vehicle Driving Noise
We conclude that NCAE as a cutting-edge model for road surface anomaly detection with 4. 20\% higher AUROC and 2. 99 times faster decision than before.
The CNN-based Coronary Occlusion Site Localization with Effective Preprocessing Method
The Coronary Artery Occlusion (CAO) acutely comes to human, and it highly threats the human's life.
Anomaly Detection in Particulate Matter Sensor using Hypothesis Pruning Generative Adversarial Network
World Health Organization (WHO) provides the guideline for managing the Particulate Matter (PM) level because when the PM level is higher, it threats the human health.
Preprocessing Method for Performance Enhancement in CNN-based STEMI Detection from 12-lead ECG
We mostly focus on enhancing the detecting performance using a preprocessing technique.
Fast Adaptive RNN Encoder-Decoder for Anomaly Detection in SMD Assembly Machine
Surface Mounted Device (SMD) assembly machine manufactures various products on a flexible manufacturing line.
Comparison of RNN Encoder-Decoder Models for Anomaly Detection
Finally, we experimentally confirmed that the model performs better when the model restores the current sequence, rather than predict the future sequence.
