Clustering and Unsupervised Anomaly Detection with L2 Normalized Deep Auto-Encoder Representations
Clustering is essential to many tasks in pattern recognition and computer vision. With the advent of deep learning, there is an increasing interest in learning deep unsupervised representations for clustering analysis. Many works on this domain rely on variants of auto-encoders and use the encoder outputs as representations/features for clustering. In this paper, we show that an l2 normalization constraint on these representations during auto-encoder training, makes the representations more separable and compact in the Euclidean space after training. This greatly improves the clustering accuracy when k-means clustering is employed on the representations. We also propose a clustering based unsupervised anomaly detection method using l2 normalized deep auto-encoder representations. We show the effect of l2 normalization on anomaly detection accuracy. We further show that the proposed anomaly detection method greatly improves accuracy compared to previously proposed deep methods such as reconstruction error based anomaly detection.
Code (0)
등록된 구현이 없습니다.
Tasks
Anomaly DetectionClusteringUnsupervised Anomaly DetectionSimilar Papers 제목 키워드 기반
Anomaly Detection by Clustering DINO Embeddings using a Dirichlet Process Mixture
In this work, we leverage informative embeddings from foundational models for unsupervised anomaly detection in medical imaging. For small datasets, a memory-bank of normative features can directly be used for anomaly de…
Unsupervised Anomaly DetectionAnomaly Clustering: Grouping Images into Coherent Clusters of Anomaly Types
We study anomaly clustering, grouping data into coherent clusters of anomaly types. This is different from anomaly detection that aims to divide anomalies from normal data. Unlike object-centered image clustering, anomal…
Anomaly DetectionClusteringDeep ClusteringImage ClusteringRUAD: unsupervised anomaly detection in HPC systems
The increasing complexity of modern high-performance computing (HPC) systems necessitates the introduction of automated and data-driven methodologies to support system administrators' effort toward increasing the system'…
Anomaly DetectionClusteringUnsupervised Anomaly DetectionDeep Autoencoders for Unsupervised Anomaly Detection in Wildfire Prediction
Wildfires pose a significantly increasing hazard to global ecosystems due to the climate crisis. Due to its complex nature, there is an urgent need for innovative approaches to wildfire prediction, such as machine learni…
Anomaly DetectionClusteringUnsupervised Anomaly DetectionGraph Regularized Autoencoder and its Application in Unsupervised Anomaly Detection
Dimensionality reduction is a crucial first step for many unsupervised learning tasks including anomaly detection and clustering. Autoencoder is a popular mechanism to accomplish dimensionality reduction. In order to mak…
Anomaly DetectionClusteringDimensionality ReductionUnsupervised Anomaly Detection