Constricting Normal Latent Space for Anomaly Detection with Normal-only Training Data

Konsep Inti

Limiting the reconstruction capability of an autoencoder using a novel latent constriction loss improves anomaly detection.

Abstrak

Abstract:

Introducing a latent constriction loss to limit AE's reconstruction capability.
No additional computational cost during test time.

Introduction:

Anomaly detection challenges due to rare occurrences.
Autoencoders encode normal representations but may generalize to anomalies.

Methodology:

Training AE on normal data and introducing a constriction loss in the latent space.

Experiments:

Evaluation on Ped2, Avenue, and ShanghaiTech datasets showing improved anomaly detection.

Comparisons:

Outperforms baseline AE and memory-based methods without extra computational costs.

Conclusion:

Proposed method effectively distorts anomalous inputs for better anomaly detection.

Statistik

Evaluations using Ped2, Avenue, and ShanghaiTech datasets demonstrate the effectiveness of the method in limiting the reconstruction capability of AE.

Kutipan

"In this work, we propose to limit the reconstruction capability of AE by introducing a novel latent constriction loss."
"Our method successfully limits the reconstruction capability of AE which demonstrates its effectiveness."

Wawasan Utama Disaring Dari

Constricting Normal Latent Space for Anomaly Detection with Normal-only Training Data

by Marcella Ast... pada arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.16270.pdf

Pertanyaan yang Lebih Dalam

How can this method be adapted for real-time anomaly detection applications

To adapt this method for real-time anomaly detection applications, several considerations need to be taken into account. Firstly, the computational efficiency of the model is crucial for real-time processing. Techniques such as optimizing the network architecture, utilizing hardware acceleration like GPUs or TPUs, and implementing parallel processing can help enhance speed.
Moreover, streamlining data preprocessing steps and feature extraction processes can reduce latency in detecting anomalies. Implementing a sliding window approach where only recent data is considered for analysis can also improve real-time performance.
Additionally, incorporating techniques like online learning or incremental training can enable the model to continuously update itself with new data streams in real time. This ensures that the anomaly detection system remains adaptive and responsive to evolving patterns of anomalies.

What are potential drawbacks or limitations of constricting the latent space in anomaly detection models

While constricting the latent space in anomaly detection models offers benefits such as improved discrimination between normal and anomalous data by limiting reconstruction capability, there are potential drawbacks and limitations to consider:

Overfitting Normal Data: Constricting the latent space too much may lead to overfitting on normal instances present in the training set. This could result in reduced generalization ability when exposed to unseen variations of normal behavior or novel types of anomalies.

Loss of Anomaly Diversity: By constraining the latent space excessively, there is a risk of losing representation capacity for diverse types of anomalies that may not conform strictly within constrained boundaries. This limitation could hinder the model's ability to detect complex or rare anomalies effectively.

Hyperparameter Sensitivity: The effectiveness of constriction loss heavily relies on hyperparameters like α (constriction norm) and λ (loss weighting). Selecting inappropriate values for these parameters might impact model performance negatively or introduce instability during training.

How might this research impact other fields beyond artificial intelligence and anomaly detection

This research on constricting latent spaces for anomaly detection has implications beyond artificial intelligence and anomaly detection fields:

Cybersecurity: The concept of restricting representations through latent space constraints can be applied in cybersecurity systems for intrusion detection purposes. By limiting reconstructions based on learned representations, it enhances security measures against cyber threats.

Healthcare Monitoring: In healthcare monitoring systems like patient health tracking devices or medical imaging analysis tools, constraining latent spaces could aid in identifying abnormal patterns early on without extensive labeled anomalous data.

Financial Fraud Detection: Applying similar techniques from this research could bolster fraud detection mechanisms within financial institutions by improving accuracy in spotting irregular transactions while minimizing false positives.

4..Manufacturing Quality Control: In manufacturing settings where quality control is paramount, leveraging constrained latent spaces can assist in identifying faulty products or deviations from standard production processes more efficiently.