AI RESEARCH
Time-reversed Flow Matching with Worst Transport in High-dimensional Latent Space for Image Anomaly Detection
arXiv CS.CV
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ArXi:2508.05461v2 Announce Type: replace Likelihood-based deep generative models have been widely investigated for Image Anomaly Detection (IAD), particularly Normalizing Flows, yet their strict architectural invertibility needs often constrain scalability, particularly in large-scale data regimes. Although time-parameterized Flow Matching (FM) serves as a scalable alternative, it remains computationally challenging in IAD due to the prohibitive costs of Jacobian-trace estimation.