2 papers
cs.LG2026
Unsupervised Anomaly Detection Using Flow Matching on Tabular Data
Philip Konz, Tejaswini Medi, Margret Keuper
Financial anomaly detection often relies on large unlabeled transaction logs, where anomalous samples may already be present during training. Such training-set contamination violat…
cs.CV2025
Decoupling High and Low Frequencies for Faithful Image Generation with Fine Details
Tejaswini Medi, Hsien-Yi Wang, Arianna Rampini +1
Latent generative models compress images into learned embeddings prior to synthesis, and the generation quality critically depends on how faithfully these embeddings preserve visua…