Sampling Strategies for Efficient Training of Deep Learning Object Detection Algorithms

• URL: http://arxiv.org/abs/2505.18302v2
• Date: Tue, 27 May 2025 19:42:32 GMT
• Title: Sampling Strategies for Efficient Training of Deep Learning Object Detection Algorithms
• Authors: Gefei Shen, Yung-Hong Sun, Yu Hen Hu, Hongrui Jiang,
• Abstract summary: Two sampling strategies are investigated to enhance efficiency in training a deep learning object detection model.<n>The first strategy is uniform sampling which seeks to obtain samples evenly yet randomly through the state space of the object dynamics.<n>The second strategy of frame difference sampling is developed to explore the temporal redundancy among successive frames in a video.
• Score: 4.609829289649562
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Two sampling strategies are investigated to enhance efficiency in training a deep learning object detection model. These sampling strategies are employed under the assumption of Lipschitz continuity of deep learning models. The first strategy is uniform sampling which seeks to obtain samples evenly yet randomly through the state space of the object dynamics. The second strategy of frame difference sampling is developed to explore the temporal redundancy among successive frames in a video. Experiment result indicates that these proposed sampling strategies provide a dataset that yields good training performance while requiring relatively few manually labelled samples.

Related papers

• Inconsistency-based Active Learning for LiDAR Object Detection [1.623951368574041]
  Deep learning models for object detection in autonomous driving have recently achieved impressive performance gains.<n>Current models require increasingly large datasets for training.<n>Active learning is a promising approach that has been extensively researched in the image domain.
  arXiv  Detail & Related papers  (2025-05-01T13:29:56Z)
• Adaptive teachers for amortized samplers [76.88721198565861]
  We propose an adaptive training distribution (the teacher) to guide the training of the primary amortized sampler (the student)<n>We validate the effectiveness of this approach in a synthetic environment designed to present an exploration challenge.
  arXiv  Detail & Related papers  (2024-10-02T11:33:13Z)
• Gradient and Uncertainty Enhanced Sequential Sampling for Global Fit [0.0]
  This paper proposes a new sampling strategy for global fit called Gradient and Uncertainty Enhanced Sequential Sampling (GUESS) We show that GUESS achieved on average the highest sample efficiency compared to other surrogate-based strategies on the tested examples.
  arXiv  Detail & Related papers  (2023-09-29T19:49:39Z)
• Analysis of Sampling Strategies for Implicit 3D Reconstruction [0.0]
  In the training process of the implicit 3D reconstruction network, the choice of spatial query points' sampling strategy affects the final performance of the model. In this work, we explored the relationship between sampling strategy and network final performance through classification analysis and experimental comparison. We also proposed two methods, linear sampling and distance mask, to improve the sampling strategy of query points, making it more general and robust.
  arXiv  Detail & Related papers  (2023-04-08T12:40:52Z)
• Towards Automated Imbalanced Learning with Deep Hierarchical Reinforcement Learning [57.163525407022966]
  Imbalanced learning is a fundamental challenge in data mining, where there is a disproportionate ratio of training samples in each class. Over-sampling is an effective technique to tackle imbalanced learning through generating synthetic samples for the minority class. We propose AutoSMOTE, an automated over-sampling algorithm that can jointly optimize different levels of decisions.
  arXiv  Detail & Related papers  (2022-08-26T04:28:01Z)
• Sampling Through the Lens of Sequential Decision Making [9.101505546901999]
  We propose a reward-guided sampling strategy called Adaptive Sample with Reward (ASR) Our approach optimally adjusts the sampling process to achieve optimal performance. Empirical results in information retrieval and clustering demonstrate ASR's superb performance across different datasets.
  arXiv  Detail & Related papers  (2022-08-17T04:01:29Z)
• Beyond Farthest Point Sampling in Point-Wise Analysis [52.218037492342546]
  We propose a novel data-driven sampler learning strategy for point-wise analysis tasks. We learn sampling and downstream applications jointly. Our experiments show that jointly learning of the sampler and task brings remarkable improvement over previous baseline methods.
  arXiv  Detail & Related papers  (2021-07-09T08:08:44Z)
• Rethinking Sampling Strategies for Unsupervised Person Re-identification [59.47536050785886]
  We analyze the reasons for the performance differences between various sampling strategies under the same framework and loss function.<n>Group sampling is proposed, which gathers samples from the same class into groups.<n>Experiments on Market-1501, DukeMTMC-reID and MSMT17 show that group sampling achieves performance comparable to state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-07-07T05:39:58Z)
• AutoSampling: Search for Effective Data Sampling Schedules [118.20014773014671]
  We propose an AutoSampling method to automatically learn sampling schedules for model training. We apply our method to a variety of image classification tasks illustrating the effectiveness of the proposed method.
  arXiv  Detail & Related papers  (2021-05-28T09:39:41Z)
• One-Shot Object Detection without Fine-Tuning [62.39210447209698]
  We introduce a two-stage model consisting of a first stage Matching-FCOS network and a second stage Structure-Aware Relation Module. We also propose novel training strategies that effectively improve detection performance. Our method exceeds the state-of-the-art one-shot performance consistently on multiple datasets.
  arXiv  Detail & Related papers  (2020-05-08T01:59:23Z)
• Efficient Deep Representation Learning by Adaptive Latent Space Sampling [16.320898678521843]
  Supervised deep learning requires a large amount of training samples with annotations, which are expensive and time-consuming to obtain. We propose a novel training framework which adaptively selects informative samples that are fed to the training process.
  arXiv  Detail & Related papers  (2020-03-19T22:17:02Z)

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.