Search Results for author:
Found 7 papers, 5 papers with code
Model Selection of Zero-shot Anomaly Detectors in the Absence of Labeled Validation Data
In this work, we propose SWSA (Selection With Synthetic Anomalies): a general-purpose framework to select image-based anomaly detectors with a generated synthetic validation set.
Deep Anomaly Detection under Labeling Budget Constraints
Selecting informative data points for expert feedback can significantly improve the performance of anomaly detection (AD) in various contexts, such as medical diagnostics or fraud detection.
Zero-Shot Anomaly Detection via Batch Normalization
Anomaly detection (AD) plays a crucial role in many safety-critical application domains.
Ranked #1 on
Unsupervised Anomaly Detection
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Unsupervised Anomaly Detection
zero-shot anomaly detection
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Latent Outlier Exposure for Anomaly Detection with Contaminated Data
We propose a strategy for training an anomaly detector in the presence of unlabeled anomalies that is compatible with a broad class of models.
Detecting and Adapting to Irregular Distribution Shifts in Bayesian Online Learning
We consider the problem of online learning in the presence of distribution shifts that occur at an unknown rate and of unknown intensity.
Variational Beam Search for Novelty Detection
We consider the problem of online learning in the presence of sudden distribution shifts, which may be hard to detect and can lead to a slow but steady degradation in model performance.
Enhanced Neural Machine Translation by Learning from Draft
Neural machine translation (NMT) has recently achieved impressive results.
