1.3k citations · 1.4k across the 6 of their papers we have counts for
4 papers · 1 filter
Optimizing PatchCore for Few/many-shot Anomaly Detection
João Santos, Triet Tran, Oliver Rippel
Few-shot anomaly detection (AD) is an emerging sub-field of general AD, and tries to distinguish between normal and anomalous data using only few selected samples. While newly prop…
Transfer Learning Gaussian Anomaly Detection by Fine-tuning Representations
Oliver Rippel, Arnav Chavan, Chucai Lei +1
Current state-of-the-art anomaly detection (AD) methods exploit the powerful representations yielded by large-scale ImageNet training. However, catastrophic forgetting prevents the…
Modeling the Distribution of Normal Data in Pre-Trained Deep Features for Anomaly Detection
Oliver Rippel, Patrick Mertens, Dorit Merhof
Anomaly Detection (AD) in images is a fundamental computer vision problem and refers to identifying images and image substructures that deviate significantly from the norm. Popular…
Image-based Survival Analysis for Lung Cancer Patients using CNNs
Christoph Haarburger, Philippe Weitz, Oliver Rippel +1
Traditional survival models such as the Cox proportional hazards model are typically based on scalar or categorical clinical features. With the advent of increasingly large image d…