Mar 18, 2024 · We present a novel unified feature reconstruction-based anomaly detection framework termed RLR (Reconstruct features from a Learnable Reference representation).
To address this issue, we propose a simple yet effective feature reconstruc- tion method based on learnable reference representation for multi-class anomaly.
Jul 16, 2024 · Unsupervised anomaly detection and localization strive to learn the patterns of normal samples from the training set then treat outliers as ...
We visualize several anomalies (Anomaly) along with their corresponding Ground Truth (GT), the detection results of UniAD. (UniAD Pred), and the detection ...
We train additional decoder models to visualize the features, allowing for a more intuitive display of the reconstructed feature effects.
Nov 9, 2024 · This paper introduces a new extension of outlier detection approaches and a new concept, class separation through variance.
Mar 19, 2024 · The RLR framework introduces learnable reference representations to compel the model to learn normal feature patterns explicitly, preventing shortcuts.
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Jul 16, 2024 · The paper presents a novel unsupervised multi-class anomaly detection method that learns a unified reference representation.
Learning Unified Reference Representation for Unsupervised Multi-class Anomaly Detection [ECCV 2024][code]; Self-supervised Feature Adaptation for 3D ...
Recall that the rationale behind unsupervised anomaly detection is to model the distribution of normal data and find a compact decision boundary as in Fig. 1a.