Classifier calibration

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

1 leaderboard table shown for this task, 1 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

1 dataset whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

19 shown of 19 papers with code (29 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Masksembles for Uncertainty Estimation
  15 Dec 2020 4 repositories listed
  Our central intuition is that there is a continuous spectrum of ensemble-like models of which MC-Dropout and Deep Ensembles are extreme examples.
• No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data
  9 Jun 2021 2 repositories listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  Motivated by the above findings, we propose a novel and simple algorithm called Classifier Calibration with Virtual Representations (CCVR), which adjusts the classifier using virtual representations sampled from an…
• PrePrompt: Predictive prompting for class incremental learning
  13 May 2025 1 repository listed
  Class Incremental Learning (CIL) based on pre-trained models offers a promising direction for open-world continual learning.
• Long-tailed Medical Diagnosis with Relation-aware Representation Learning and Iterative Classifier Calibration
  5 Feb 2025 1 repository listed
  Existing works formulated this challenge as a long-tailed problem and attempted to tackle it by decoupling the feature representation and classification.
• Enhancing Generalized Few-Shot Semantic Segmentation via Effective Knowledge Transfer
  20 Dec 2024 1 repository listed
  Furthermore, existing GFSS approaches suffer from a lack of contextual information for novel classes due to their limited samples, we thereby introduce a context consistency learning scheme to transfer the contextual…
• Improved User Identification through Calibrated Monte-Carlo Dropout
  29 Sep 2024 1 repository listed
  Our thorough evaluation and comparison of different calibration methods have shown improved accuracy in user identification across multiple datasets.
• Accuracy-Preserving Calibration via Statistical Modeling on Probability Simplex
  21 Feb 2024 1 repository listed
  We propose an accuracy-preserving calibration method using the Concrete distribution as the probabilistic model on the probability simplex.
• No Fear of Classifier Biases: Neural Collapse Inspired Federated Learning with Synthetic and Fixed Classifier
  17 Mar 2023 1 repository listed Syntology ran 2 of 2 samples · 0 unverified
  Recent advances in neural collapse have shown that the classifiers and feature prototypes under perfect training scenarios collapse into an optimal structure called simplex equiangular tight frame (ETF).
• Expeditious Saliency-guided Mix-up through Random Gradient Thresholding
  9 Dec 2022 1 repository listed
  Mix-up training approaches have proven to be effective in improving the generalization ability of Deep Neural Networks.
• FedFA: Federated Learning with Feature Anchors to Align Features and Classifiers for Heterogeneous Data
  17 Nov 2022 1 repository listed
  This enables client models to be updated in a shared feature space with consistent classifiers during local training.
• Packed-Ensembles for Efficient Uncertainty Estimation
  17 Oct 2022 1 repository listed Syntology ran 1 of 3 samples · 2 unverified
  Deep Ensembles (DE) are a prominent approach for achieving excellent performance on key metrics such as accuracy, calibration, uncertainty estimation, and out-of-distribution detection.
• Class-wise and reduced calibration methods
  7 Oct 2022 1 repository listed
  We prove for several notions of calibration that solving the reduced problem minimizes the corresponding notion of miscalibration in the full problem, allowing the use of non-parametric recalibration methods that fail…
• Hidden Heterogeneity: When to Choose Similarity-Based Calibration
  3 Feb 2022 1 repository listed
  However, these methods are unable to detect subpopulations where calibration could also improve prediction accuracy.
• Generalized and Incremental Few-Shot Learning by Explicit Learning and Calibration without Forgetting
  18 Aug 2021 1 repository listed Syntology ran 9 of 13 samples · 4 unverified
  Both generalized and incremental few-shot learning have to deal with three major challenges: learning novel classes from only few samples per class, preventing catastrophic forgetting of base classes, and classifier…
• Danish Fungi 2020 -- Not Just Another Image Recognition Dataset
  18 Mar 2021 1 repository listed
  Interestingly, ViT achieves results superior to CNN baselines with 80.
• Classifier Calibration: with application to threat scores in cybersecurity
  9 Feb 2021 1 repository listed
  A calibrator is a function that maps the arbitrary classifier score, of a testing observation, onto [0, 1] to provide an estimate for the posterior probability of belonging to one of the two classes.
• How Well Do Self-Supervised Models Transfer?
  26 Nov 2020 1 repository listed Syntology ran 4 of 5 samples · 1 unverified
  We evaluate the transfer performance of 13 top self-supervised models on 40 downstream tasks, including many-shot and few-shot recognition, object detection, and dense prediction.
• Multivariate Confidence Calibration for Object Detection
  28 Apr 2020 1 repository listed Syntology ran 0 of 3 samples · 3 unverified
  Therefore, we present a novel framework to measure and calibrate biased (or miscalibrated) confidence estimates of object detection methods.
• Multi-class probabilistic classification using inductive and cross Venn-Abers predictors
  16 Jun 2017 1 repository listed
  Inductive (IVAP) and cross (CVAP) Venn–Abers predictors are computationally efficient algorithms for probabilistic prediction in binary classification problems.

Syntology lines on 6 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.