Active Learning

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

16 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

1 subtask in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

30 shown of 913 papers with code (3,073 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.

• Active Learning for Convolutional Neural Networks: A Core-Set Approach
  1 Aug 2017 11 repositories listed Syntology ran 2 of 4 samples · 2 unverified · 4 pointer-only (licence)
  active learning).
• Self-Regulated Interactive Sequence-to-Sequence Learning
  11 Jul 2019 7 repositories listed
  Not all types of supervision signals are created equal: Different types of feedback have different costs and effects on learning.
• Learning Loss for Active Learning
  9 May 2019 7 repositories listed
  In this paper, we propose a novel active learning method that is simple but task-agnostic, and works efficiently with the deep networks.
• Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds
  9 Jun 2019 6 repositories listed Syntology ran 2 of 2 samples · 0 unverified · 2 pointer-only (licence)
  We design a new algorithm for batch active learning with deep neural network models.
• Variational Adversarial Active Learning
  31 Mar 2019 6 repositories listed Syntology ran 1 of 14 samples · 13 unverified
  Unlike conventional active learning algorithms, our approach is task agnostic, i.
• Few-Shot Learning with Graph Neural Networks
  10 Nov 2017 6 repositories listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  We propose to study the problem of few-shot learning with the prism of inference on a partially observed graphical model, constructed from a collection of input images whose label can be either observed or not.
• Deep Bayesian Active Learning with Image Data
  8 Mar 2017 6 repositories listed Syntology ran 0 of 7 samples · 7 unverified
  In this paper we combine recent advances in Bayesian deep learning into the active learning framework in a practical way.
• libact: Pool-based Active Learning in Python
  1 Oct 2017 5 repositories listed
  libact is a Python package designed to make active learning easier for general users.
• Active learning in annotating micro-blogs dealing with e-reputation
  16 Jun 2017 5 repositories listed
  This paper intends to develop a so-called active learning process for automatically annotating French language tweets that deal with the image (i.
• Active Learning by Feature Mixing
  14 Mar 2022 4 repositories listed Syntology ran 7 of 8 samples · 1 unverified · 2 pointer-only (licence)
  We identify unlabelled instances with sufficiently-distinct features by seeking inconsistencies in predictions resulting from interventions on their representations.
• Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data
  3 Nov 2021 4 repositories listed Syntology ran 7 of 11 samples · 4 unverified · 1 pointer-only (licence)
  We introduce causal, Bayesian acquisition functions grounded in information theory that bias data acquisition towards regions with overlapping support to maximize sample efficiency for learning personalized treatment…
• Deep Deterministic Uncertainty: A Simple Baseline
  23 Feb 2021 4 repositories listed
  Reliable uncertainty from deterministic single-forward pass models is sought after because conventional methods of uncertainty quantification are computationally expensive.
• Synbols: Probing Learning Algorithms with Synthetic Datasets
  14 Sep 2020 4 repositories listed
  Progress in the field of machine learning has been fueled by the introduction of benchmark datasets pushing the limits of existing algorithms.
• Iterative Surrogate Model Optimization (ISMO): An active learning algorithm for PDE constrained optimization with deep neural networks
  13 Aug 2020 4 repositories listed
  We present a novel active learning algorithm, termed as iterative surrogate model optimization (ISMO), for robust and efficient numerical approximation of PDE constrained optimization problems.
• Cost-Effective Active Learning for Deep Image Classification
  13 Jan 2017 4 repositories listed Syntology ran 2 of 13 samples · 11 unverified · 2 pointer-only (licence)
  In this paper, we propose a novel active learning framework, which is capable of building a competitive classifier with optimal feature representation via a limited amount of labeled training instances in an incremental…
• TacoGFN: Target-conditioned GFlowNet for Structure-based Drug Design
  5 Oct 2023 3 repositories listed Syntology ran 6 of 10 samples · 4 unverified · 1 pointer-only (licence)
  We design TacoGFN, a novel GFlowNet-based approach for structure-based drug design, which can generate molecules conditioned on any protein pocket structure with probabilities proportional to its affinity and property…
• Let's Verify Step by Step
  31 May 2023 3 repositories listed Syntology ran 3 of 3 samples · 0 unverified
  We conduct our own investigation, finding that process supervision significantly outperforms outcome supervision for training models to solve problems from the challenging MATH dataset.
• Creating Custom Event Data Without Dictionaries: A Bag-of-Tricks
  3 Apr 2023 3 repositories listed
  Event data, or structured records of ``who did what to whom'' that are automatically extracted from text, is an important source of data for scholars of international politics.
• Black-Box Batch Active Learning for Regression
  17 Feb 2023 3 repositories listed Syntology ran 8 of 13 samples · 5 unverified · 8 pointer-only (licence)
  This approach is compatible with a wide range of machine learning models, including regular and Bayesian deep learning models and non-differentiable models such as random forests.
• Sample-Efficient Multi-Objective Learning via Generalized Policy Improvement Prioritization
  18 Jan 2023 3 repositories listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  Finally, we introduce a bound that characterizes the maximum utility loss (with respect to the optimal solution) incurred by the partial solutions computed by our method throughout learning.
• Simple Techniques Work Surprisingly Well for Neural Network Test Prioritization and Active Learning (Replicability Study)
  2 May 2022 3 repositories listed Syntology ran 0 of 6 samples · 6 unverified
  Test Input Prioritizers (TIP) for Deep Neural Networks (DNN) are an important technique to handle the typically very large test datasets efficiently, saving computation and labeling costs.
• Active learning with MaskAL reduces annotation effort for training Mask R-CNN
  13 Dec 2021 3 repositories listed
  In our study, MaskAL was compared to a random sampling method on a broccoli dataset with five visually similar classes.
• Bayesian Uncertainty and Expected Gradient Length -- Regression: Two Sides Of The Same Coin?
  19 Apr 2021 3 repositories listed
  Subsequently, we show that expected gradient length in regression is equivalent to Bayesian uncertainty.
• Can Active Learning Preemptively Mitigate Fairness Issues?
  14 Apr 2021 3 repositories listed Syntology ran 0 of 1 samples · 1 unverified
  We also explore the interaction of algorithmic fairness methods such as gradient reversal (GRAD) and BALD.
• Bayesian Force Fields from Active Learning for Simulation of Inter-Dimensional Transformation of Stanene
  26 Aug 2020 3 repositories listed
  We present a way to dramatically accelerate Gaussian process models for interatomic force fields based on many-body kernels by mapping both forces and uncertainties onto functions of low-dimensional features.
• Deep Active Learning for Axon-Myelin Segmentation on Histology Data
  11 Jul 2019 3 repositories listed
  In this paper we provide a framework for Deep Active Learning applied to a real-world scenario.
• BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning
  19 Jun 2019 3 repositories listed Syntology ran 1 of 8 samples · 7 unverified
  We develop BatchBALD, a tractable approximation to the mutual information between a batch of points and model parameters, which we use as an acquisition function to select multiple informative points jointly for the…
• ALiPy: Active Learning in Python
  12 Jan 2019 3 repositories listed
  Supervised machine learning methods usually require a large set of labeled examples for model training.
• An Overview and a Benchmark of Active Learning for Outlier Detection with One-Class Classifiers
  14 Aug 2018 3 repositories listed
  This article starts with a categorization of the various methods.
• Less is more: sampling chemical space with active learning
  28 Jan 2018 3 repositories listed
  In this work, we present a fully automated approach for the generation of datasets with the intent of training universal ML potentials.

Syntology lines on 15 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.