🤖 AI Summary
This work addresses the challenge of detecting out-of-distribution (OOD) inputs in deployed machine learning models, which often suffer from performance degradation under distributional shift. The authors propose TV-OOD, a novel OOD detection method that, for the first time, leverages Total Variation (TV) as a core principle. By employing a Total Variation network estimator to quantify each input’s contribution to the overall total variation, TV-OOD constructs a detection criterion that requires neither additional training nor generative models. Extensive experiments across multiple image classification architectures and standard benchmark datasets demonstrate that TV-OOD achieves performance comparable to or better than current state-of-the-art methods under common OOD evaluation metrics, thereby confirming its effectiveness and broad applicability.
📝 Abstract
This paper introduces a novel approach to securing machine learning model deployments against potential distribution shifts in practical applications, the Total Variation Out-of-Distribution (TV-OOD) detection method. Existing methods have produced satisfactory results, but TV-OOD improves upon these by leveraging the Total Variation Network Estimator to calculate each input's contribution to the overall total variation. By defining this as the total variation score, TV-OOD discriminates between in- and out-of-distribution data. The method's efficacy was tested across a range of models and datasets, consistently yielding results in image classification tasks that were either comparable or superior to those achieved by leading-edge out-of-distribution detection techniques across all evaluation metrics.