Classifier calibration
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Benchmarks
CIFAR-100
Most implemented
Masksembles for Uncertainty Estimation
No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data
PrePrompt: Predictive prompting for class incremental learning
Long-tailed Medical Diagnosis with Relation-aware Representation Learning and Iterative Classifier Calibration
Enhancing Generalized Few-Shot Semantic Segmentation via Effective Knowledge Transfer
Improved User Identification through Calibrated Monte-Carlo Dropout
Papers
Full-range Binary Classifier Calibration for Stable Model Updates in Production
Detection models running in adversarial environments face a malicious distribution that drifts rapidly while the benign distribution stays comparatively stable, so teams retrain and redeploy constantly to stay ahead of n…
Classifier calibrationSimpson's Paradox in Behavioral Curves: How Aggregation Distorts Parametric Models of User Dynamics
Behavioral curve modeling -- fitting parametric functions to engagement-versus-exposure data -- is standard practice in recommendation, advertising, and clinical dosing. We show that aggregation introduces a systematic d…
Classifier calibrationToward Optimal Sampling Rate Selection and Unbiased Classification for Precise Animal Activity Recognition
With the rapid advancements in deep learning techniques, wearable sensor-aided animal activity recognition (AAR) has demonstrated promising performance, thereby improving livestock management efficiency as well as animal…
Classifier calibrationActivity RecognitionImpact of domain adaptation in deep learning for medical image classifications
Domain adaptation (DA) is a quickly expanding area in machine learning that involves adjusting a model trained in one domain to perform well in another domain. While there have been notable progressions, the fundamental …
Skin Cancer ClassificationClassifier calibrationFederated LearningDomain AdaptationClassifier Calibration at Scale: An Empirical Study of Model-Agnostic Post-Hoc Methods
We study model-agnostic post-hoc calibration methods intended to improve probabilistic predictions in supervised binary classification on real i.i.d. tabular data, with particular emphasis on conformal and Venn-based app…
Classifier calibrationBinary ClassificationConformal Safety Monitoring for Flight Testing: A Case Study in Data-Driven Safety Learning
We develop a data-driven approach for runtime safety monitoring in flight testing, where pilots perform maneuvers on aircraft with uncertain parameters. Because safety violations can arise unexpectedly as a result of the…
Classifier calibration