Active Learning Methods for Efficient Data Utilization and Model Performance Enhancement

• URL: http://arxiv.org/abs/2504.16136v1
• Date: Mon, 21 Apr 2025 20:42:13 GMT
• Title: Active Learning Methods for Efficient Data Utilization and Model Performance Enhancement
• Authors: Chiung-Yi Tseng, Junhao Song, Ziqian Bi, Tianyang Wang, Chia Xin Liang, Ming Liu,
• Abstract summary: This paper gives a detailed overview of Active Learning (AL), which is a strategy in machine learning that helps models achieve better performance using fewer labeled examples.<n>It introduces the basic concepts of AL and discusses how it is used in various fields such as computer vision, natural language processing, transfer learning, and real-world applications.
• Score: 5.4044723481768235
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In the era of data-driven intelligence, the paradox of data abundance and annotation scarcity has emerged as a critical bottleneck in the advancement of machine learning. This paper gives a detailed overview of Active Learning (AL), which is a strategy in machine learning that helps models achieve better performance using fewer labeled examples. It introduces the basic concepts of AL and discusses how it is used in various fields such as computer vision, natural language processing, transfer learning, and real-world applications. The paper focuses on important research topics such as uncertainty estimation, handling of class imbalance, domain adaptation, fairness, and the creation of strong evaluation metrics and benchmarks. It also shows that learning methods inspired by humans and guided by questions can improve data efficiency and help models learn more effectively. In addition, this paper talks about current challenges in the field, including the need to rebuild trust, ensure reproducibility, and deal with inconsistent methodologies. It points out that AL often gives better results than passive learning, especially when good evaluation measures are used. This work aims to be useful for both researchers and practitioners by providing key insights and proposing directions for future progress in active learning.

Related papers

• KBAlign: Efficient Self Adaptation on Specific Knowledge Bases [73.34893326181046]
  Large language models (LLMs) usually rely on retrieval-augmented generation to exploit knowledge materials in an instant manner.<n>We propose KBAlign, an approach designed for efficient adaptation to downstream tasks involving knowledge bases.<n>Our method utilizes iterative training with self-annotated data such as Q&A pairs and revision suggestions, enabling the model to grasp the knowledge content efficiently.
  arXiv  Detail & Related papers  (2024-11-22T08:21:03Z)
• Towards Effective Evaluations and Comparisons for LLM Unlearning Methods [97.2995389188179]
  This paper seeks to refine the evaluation of machine unlearning for large language models.<n>It addresses two key challenges -- the robustness of evaluation metrics and the trade-offs between competing goals.
  arXiv  Detail & Related papers  (2024-06-13T14:41:00Z)
• Towards Automated Knowledge Integration From Human-Interpretable Representations [55.2480439325792]
  We introduce and motivate theoretically the principles of informed meta-learning enabling automated and controllable inductive bias selection. We empirically demonstrate the potential benefits and limitations of informed meta-learning in improving data efficiency and generalisation.
  arXiv  Detail & Related papers  (2024-02-25T15:08:37Z)
• Compute-Efficient Active Learning [0.0]
  Active learning aims at reducing labeling costs by selecting the most informative samples from an unlabeled dataset. Traditional active learning process often demands extensive computational resources, hindering scalability and efficiency. We present a novel method designed to alleviate the computational burden associated with active learning on massive datasets.
  arXiv  Detail & Related papers  (2024-01-15T12:32:07Z)
• Advancing Deep Active Learning & Data Subset Selection: Unifying Principles with Information-Theory Intuitions [3.0539022029583953]
  This thesis aims to enhance the practicality of deep learning by improving the label and training efficiency of deep learning models. We investigate data subset selection techniques, specifically active learning and active sampling, grounded in information-theoretic principles.
  arXiv  Detail & Related papers  (2024-01-09T01:41:36Z)
• Learning to Rank for Active Learning via Multi-Task Bilevel Optimization [29.207101107965563]
  We propose a novel approach for active learning, which aims to select batches of unlabeled instances through a learned surrogate model for data acquisition. A key challenge in this approach is developing an acquisition function that generalizes well, as the history of data, which forms part of the utility function's input, grows over time.
  arXiv  Detail & Related papers  (2023-10-25T22:50:09Z)
• Learning Objective-Specific Active Learning Strategies with Attentive Neural Processes [72.75421975804132]
  Learning Active Learning (LAL) suggests to learn the active learning strategy itself, allowing it to adapt to the given setting. We propose a novel LAL method for classification that exploits symmetry and independence properties of the active learning problem. Our approach is based on learning from a myopic oracle, which gives our model the ability to adapt to non-standard objectives.
  arXiv  Detail & Related papers  (2023-09-11T14:16:37Z)
• What Makes Good Contrastive Learning on Small-Scale Wearable-based Tasks? [59.51457877578138]
  We study contrastive learning on the wearable-based activity recognition task. This paper presents an open-source PyTorch library textttCL-HAR, which can serve as a practical tool for researchers.
  arXiv  Detail & Related papers  (2022-02-12T06:10:15Z)
• Learnability of Learning Performance and Its Application to Data Valuation [11.78594243870616]
  In most machine learning (ML) tasks, evaluating learning performance on a given dataset requires intensive computation. The ability to efficiently estimate learning performance may benefit a wide spectrum of applications, such as active learning, data quality management, and data valuation. Recent empirical studies show that for many common ML models, one can accurately learn a parametric model that predicts learning performance for any given input datasets using a small amount of samples.
  arXiv  Detail & Related papers  (2021-07-13T18:56:04Z)
• Bayesian active learning for production, a systematic study and a reusable library [85.32971950095742]
  In this paper, we analyse the main drawbacks of current active learning techniques. We do a systematic study on the effects of the most common issues of real-world datasets on the deep active learning process. We derive two techniques that can speed up the active learning loop such as partial uncertainty sampling and larger query size.
  arXiv  Detail & Related papers  (2020-06-17T14:51:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.