METHODS AND APPARATUS FOR MULTI-MODAL ANOMALY DETECTION

Smart overview of the Invention

Overview: The patent application describes an apparatus designed for multi-modal anomaly detection, utilizing interface circuitry and machine-readable instructions. The system generates a probability distribution from latent embeddings extracted from datasets. This distribution captures interactions between various data modalities, such as text, images, and audio, and is used to compute an anomaly detection score. This score identifies anomalies in single or multiple data modalities.

Background: Anomaly detection techniques are crucial for identifying data irregularities that deviate from expected patterns, which can improve data quality. These techniques are often combined with machine learning to learn patterns from historical data and identify deviations in real-time. Effective anomaly detection is vital in sectors like manufacturing, quality control, and deepfake detection, where ensuring data integrity and reliability is paramount.

Detailed Description: Anomalies are data points that significantly deviate from expected behaviors, including outliers, event changes, and drifts. Traditional anomaly detection systems use statistical methods and machine learning algorithms to identify these deviations. Multi-modal anomaly detection involves analyzing data from various modalities and is often limited by existing algorithms' reliance on single predictions. The patent introduces Decomposable Probabilistic Multi-Modal Anomaly Detection (DP-MMAD), which enhances robustness and explainability by considering modality interactions and importance.

Technical Approach: DP-MMAD employs Gaussian Mixture Models (GMMs) to fit modality-specific latent embeddings and joint GMMs for combinations of all modalities. This approach identifies the importance of each modality, affecting the anomaly detection process. The system computes decomposable anomaly detection scores using Mahalanobis distances, allowing for consistent and accurate anomaly detection. This method is applicable across various domains, including manufacturing and deepfake detection, enhancing efficiency and cost-effectiveness.

Applications and Benefits: The DP-MMAD framework supports continual learning and improves anomaly detection systems through Explainable AI (XAI) and Human-in-the-Loop (HITL) methodologies. By modeling comprehensive probabilistic interactions across modalities, DP-MMAD provides fine-grained structural analyses and system-level insights. This enriches the explainability and effectiveness of multi-modal anomaly detection systems, promoting AI-assisted decision-making in diverse fields like cybersecurity and predictive maintenance.