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Found 5 papers, 3 papers with code
ValUES: A Framework for Systematic Validation of Uncertainty Estimation in Semantic Segmentation
Which components of an uncertainty method are essential for real-world performance?
cOOpD: Reformulating COPD classification on chest CT scans as anomaly detection using contrastive representations
We reformulate COPD binary classification as an anomaly detection task, proposing cOOpD: heterogeneous pathological regions are detected as Out-of-Distribution (OOD) from normal homogeneous lung regions.
Navigating the Pitfalls of Active Learning Evaluation: A Systematic Framework for Meaningful Performance Assessment
Thus, today's AL literature presents an inconsistent and contradictory landscape, leaving practitioners uncertain about whether and how to use AL in their tasks.
CRADL: Contrastive Representations for Unsupervised Anomaly Detection and Localization
By utilizing the representations of contrastive learning, we aim to fix the over-fixation on low-level features and learn more semantic-rich representations.
A Call to Reflect on Evaluation Practices for Failure Detection in Image Classification
To demonstrate the relevance of this unified perspective, we present a large-scale empirical study for the first time enabling benchmarking confidence scoring functions w. r. t all relevant methods and failure sources.
