arXiv:2606.00684v1 Announce Type: cross Abstract: We address the problem of out-of-distribution (OOD) detection for target observations embedded in a subspace of the high dimensional data space. Using continuous normalizing flows (CNFs), we propose a Lagrangian sub-flow (LSF) framework designed to isolate and estimate the density for the relevant components in the representation and using the remaining components as context. Through experimentation with models for speech synthesis, we show that CNFs, similarly to other deep generative models (DGMs), are susceptible to the "likelihood paradox",
The continuous development in AI and machine learning, specifically in deep generative models, necessitates better methods for anomaly detection and ensuring model robustness.
Improving out-of-distribution detection is crucial for the reliability and safety of AI applications, especially as they become integrated into critical systems.
New methods like Lagrangian sub-flow promise more robust OOD detection for deep generative models, addressing a significant limitation currently observed in their deployment.
- · AI safety researchers
- · Generative AI developers
- · High-stakes AI applications (e.g., medical, autonomous vehicles)
- · AI systems prone to 'likelihood paradox'
Improved OOD detection strengthens the trustworthiness and deployability of deep generative models.
This could lead to broader adoption of AI in sensitive areas where robustness against unexpected inputs is paramount.
More reliable AI systems might accelerate the development of agentic AI and autonomous decision-making in complex environments.
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Read at arXiv cs.CL