Layer-Specific Lipschitz Modulation for Fault-Tolerant Multimodal Representation Learning

2026-03-26

AI Summary

This work addresses the robustness of multimodal systems under anomalies such as sensor failures, signal degradation, or cross-modal inconsistencies by proposing a unified framework that integrates self-supervised anomaly detection with error correction. The approach leverages perturbation propagation theory to design a two-stage training procedure: first, a multimodal convolutional autoencoder preserves anomaly signatures in the latent space; second, a learnable correction module, trained with contrastive learning, refines the corrupted representations. Innovatively, layer-specific Lipschitz modulation and gradient clipping mechanisms are introduced to theoretically bound the model’s sensitivity to local faults, thereby unifying analytical robustness guarantees with practical fault tolerance. Experiments demonstrate that the proposed method significantly improves both anomaly detection accuracy and reconstruction quality of degraded signals on multimodal fault datasets.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Security and Privacy: Large-scale security measurements

Abstract

Modern multimodal systems deployed in industrial and safety-critical environments must remain reliable under partial sensor failures, signal degradation, or cross-modal inconsistencies. This work introduces a mathematically grounded framework for fault-tolerant multimodal representation learning that unifies self-supervised anomaly detection and error correction within a single architecture. Building upon a theoretical analysis of perturbation propagation, we derive Lipschitz- and Jacobian-based criteria that determine whether a neural operator amplifies or attenuates localized faults. Guided by this theory, we propose a two-stage self-supervised training scheme: pre-training a multimodal convolutional autoencoder on clean data to preserve localized anomaly signals in the latent space, and expanding it with a learnable compute block composed of dense layers for correction and contrastive objectives for anomaly identification. Furthermore, we introduce layer-specific Lipschitz modulation and gradient clipping as principled mechanisms to control sensitivity across detection and correction modules. Experimental results on multimodal fault datasets demonstrate that the proposed approach improves both anomaly detection accuracy and reconstruction under sensor corruption. Overall, this framework bridges the gap between analytical robustness guarantees and practical fault-tolerant multimodal learning.

Problem

• fault-tolerant
• multimodal representation learning
• sensor failures
• anomaly detection
• robustness

Innovation

• Lipschitz modulation
• fault-tolerant learning
• multimodal representation
• self-supervised anomaly detection
• perturbation propagation