Are Candidate Models Really Needed for Active Learning?

• URL: http://arxiv.org/abs/2605.14689v1
• Date: Thu, 14 May 2026 11:03:29 GMT
• Title: Are Candidate Models Really Needed for Active Learning?
• Abstract summary: This study investigates the feasibility of using CNNs and transformers with randomly selecting weights.<n>We evaluate three confidence-based sampling strategies: high confidence (HC), low confidence (LC), and a combination of high confidence in the early stages of training.
• Score: 0.7176906280023595
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning has profoundly impacted domains such as computer vision and natural language processing by uncovering complex patterns in vast datasets. However, the reliance on extensive labeled data poses significant challenges, including resource constraints and annotation errors, particularly in training Convolutional Neural Networks (CNNs) and transformers due to a larger number of parameters. Active learning offers a promising solution to reduce labeling burdens by strategically selecting the most informative samples for annotation. However, the current active learning frameworks are time-intensive which select the samples iteratively with the help of initial candidate models. This study investigates the feasibility of using CNNs and transformers with randomly initialized weights, eliminating the need for initial candidate models while achieving results comparable to active learning frameworks that depend on such candidate models. We evaluate three confidence-based sampling strategies: high confidence (HC), low confidence (LC), and a combination of high confidence in the early stages of training and low confidence at later stages of training (HCLC). Among these, mostly LC demonstrated the best performance in our experiments, showcasing its effectiveness as an active learning strategy without the need for candidate models. Further, extensive experiments verify the robustness of the proposed active learning methods. By challenging traditional frameworks, the proposed work introduces a streamlined approach to active learning, advancing efficiency and flexibility across diverse datasets and domains.

Related papers

• Learning What Matters: Prioritized Concept Learning via Relative Error-driven Sample Selection [38.35524024887503]
  We propose PRioritized cOncept learninG via Relative Error-driven Sample Selection (PROGRESS)<n>PROGRESS is a data- and compute-efficient framework that enables vision-language models to dynamically select what to learn next.<n>We show that PROGRESS consistently outperforms state-of-the-art baselines with much less data and supervision.
  arXiv  Detail & Related papers  (2025-06-01T17:05:35Z)
• Image Classification with Deep Reinforcement Active Learning [28.924413229981827]
  In many real-world scenarios, labeled data are scarce, and their hand-labeling is time, effort and cost demanding.<n>Active learning is an alternative paradigm that mitigates the effort in hand-labeling data, and annotated by an expert.<n>In this work, we devise an adaptive active learning method based on Markov Decision Process (MDP)
  arXiv  Detail & Related papers  (2024-12-27T18:37:51Z)
• Active Prompt Learning with Vision-Language Model Priors [9.173468790066956]
  We introduce a class-guided clustering that leverages the pre-trained image and text encoders of vision-language models. We propose a budget-saving selective querying based on adaptive class-wise thresholds.
  arXiv  Detail & Related papers  (2024-11-23T02:34:33Z)
• Learn while Unlearn: An Iterative Unlearning Framework for Generative Language Models [52.40798352740857]
  We introduce the Iterative Contrastive Unlearning (ICU) framework, which consists of three core components.<n>A Knowledge Unlearning Induction module targets specific knowledge for removal using an unlearning loss.<n>A Contrastive Learning Enhancement module preserves the model's expressive capabilities against the pure unlearning goal.<n>An Iterative Unlearning Refinement module dynamically adjusts the unlearning process through ongoing evaluation and updates.
  arXiv  Detail & Related papers  (2024-07-25T07:09:35Z)
• 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)
• Frugal Reinforcement-based Active Learning [12.18340575383456]
  We propose a novel active learning approach for label-efficient training. The proposed method is iterative and aims at minimizing a constrained objective function that mixes diversity, representativity and uncertainty criteria. We also introduce a novel weighting mechanism based on reinforcement learning, which adaptively balances these criteria at each training iteration.
  arXiv  Detail & Related papers  (2022-12-09T14:17:45Z)
• Responsible Active Learning via Human-in-the-loop Peer Study [88.01358655203441]
  We propose a responsible active learning method, namely Peer Study Learning (PSL), to simultaneously preserve data privacy and improve model stability. We first introduce a human-in-the-loop teacher-student architecture to isolate unlabelled data from the task learner (teacher) on the cloud-side. During training, the task learner instructs the light-weight active learner which then provides feedback on the active sampling criterion.
  arXiv  Detail & Related papers  (2022-11-24T13:18:27Z)
• Towards Robust Dataset Learning [90.2590325441068]
  We propose a principled, tri-level optimization to formulate the robust dataset learning problem. Under an abstraction model that characterizes robust vs. non-robust features, the proposed method provably learns a robust dataset.
  arXiv  Detail & Related papers  (2022-11-19T17:06:10Z)
• Knowledge-driven Active Learning [70.37119719069499]
  Active learning strategies aim at minimizing the amount of labelled data required to train a Deep Learning model. Most active strategies are based on uncertain sample selection, and even often restricted to samples lying close to the decision boundary. Here we propose to take into consideration common domain-knowledge and enable non-expert users to train a model with fewer samples.
  arXiv  Detail & Related papers  (2021-10-15T06:11:53Z)
• Active Learning for Sequence Tagging with Deep Pre-trained Models and Bayesian Uncertainty Estimates [52.164757178369804]
  Recent advances in transfer learning for natural language processing in conjunction with active learning open the possibility to significantly reduce the necessary annotation budget. We conduct an empirical study of various Bayesian uncertainty estimation methods and Monte Carlo dropout options for deep pre-trained models in the active learning framework. We also demonstrate that to acquire instances during active learning, a full-size Transformer can be substituted with a distilled version, which yields better computational performance.
  arXiv  Detail & Related papers  (2021-01-20T13:59:25Z)
• Ask-n-Learn: Active Learning via Reliable Gradient Representations for Image Classification [29.43017692274488]
  Deep predictive models rely on human supervision in the form of labeled training data. We propose Ask-n-Learn, an active learning approach based on gradient embeddings obtained using the pesudo-labels estimated in each of the algorithm.
  arXiv  Detail & Related papers  (2020-09-30T05:19:56Z)

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.