Diversified Batch Selection for Training Acceleration

• URL: http://arxiv.org/abs/2406.04872v1
• Date: Fri, 7 Jun 2024 12:12:20 GMT
• Title: Diversified Batch Selection for Training Acceleration
• Authors: Feng Hong, Yueming Lyu, Jiangchao Yao, Ya Zhang, Ivor W. Tsang, Yanfeng Wang,
• Abstract summary: A prevalent research line, known as online batch selection, explores selecting informative subsets during the training process. vanilla reference-model-free methods involve independently scoring and selecting data in a sample-wise manner. We propose Diversified Batch Selection (DivBS), which is reference-model-free and can efficiently select diverse and representative samples.
• Score: 68.67164304377732
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The remarkable success of modern machine learning models on large datasets often demands extensive training time and resource consumption. To save cost, a prevalent research line, known as online batch selection, explores selecting informative subsets during the training process. Although recent efforts achieve advancements by measuring the impact of each sample on generalization, their reliance on additional reference models inherently limits their practical applications, when there are no such ideal models available. On the other hand, the vanilla reference-model-free methods involve independently scoring and selecting data in a sample-wise manner, which sacrifices the diversity and induces the redundancy. To tackle this dilemma, we propose Diversified Batch Selection (DivBS), which is reference-model-free and can efficiently select diverse and representative samples. Specifically, we define a novel selection objective that measures the group-wise orthogonalized representativeness to combat the redundancy issue of previous sample-wise criteria, and provide a principled selection-efficient realization. Extensive experiments across various tasks demonstrate the significant superiority of DivBS in the performance-speedup trade-off. The code is publicly available.

Related papers

• Adapt-$\infty$: Scalable Lifelong Multimodal Instruction Tuning via Dynamic Data Selection [89.42023974249122]
  Adapt-$infty$ is a new multi-way and adaptive data selection approach for Lifelong Instruction Tuning. We construct pseudo-skill clusters by grouping gradient-based sample vectors. We select the best-performing data selector for each skill cluster from a pool of selector experts.
  arXiv  Detail & Related papers  (2024-10-14T15:48:09Z)
• Towards Free Data Selection with General-Purpose Models [71.92151210413374]
  A desirable data selection algorithm can efficiently choose the most informative samples to maximize the utility of limited annotation budgets. Current approaches, represented by active learning methods, typically follow a cumbersome pipeline that iterates the time-consuming model training and batch data selection repeatedly. FreeSel bypasses the heavy batch selection process, achieving a significant improvement in efficiency and being 530x faster than existing active learning methods.
  arXiv  Detail & Related papers  (2023-09-29T15:50:14Z)
• AdaSelection: Accelerating Deep Learning Training through Data Subsampling [27.46630703428186]
  We introduce AdaSelection, an adaptive sub-sampling method to identify the most informative sub-samples within each minibatch. Compared with industry-standard baselines, AdaSelection consistently displays superior performance.
  arXiv  Detail & Related papers  (2023-06-19T07:01:28Z)
• 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)
• SelectAugment: Hierarchical Deterministic Sample Selection for Data Augmentation [72.58308581812149]
  We propose an effective approach, dubbed SelectAugment, to select samples to be augmented in a deterministic and online manner. Specifically, in each batch, we first determine the augmentation ratio, and then decide whether to augment each training sample under this ratio. In this way, the negative effects of the randomness in selecting samples to augment can be effectively alleviated and the effectiveness of DA is improved.
  arXiv  Detail & Related papers  (2021-12-06T08:38:38Z)
• One for More: Selecting Generalizable Samples for Generalizable ReID Model [92.40951770273972]
  This paper proposes a one-for-more training objective that takes the generalization ability of selected samples as a loss function. Our proposed one-for-more based sampler can be seamlessly integrated into the ReID training framework.
  arXiv  Detail & Related papers  (2020-12-10T06:37:09Z)
• Online Active Model Selection for Pre-trained Classifiers [72.84853880948894]
  We design an online selective sampling approach that actively selects informative examples to label and outputs the best model with high probability at any round. Our algorithm can be used for online prediction tasks for both adversarial and streams.
  arXiv  Detail & Related papers  (2020-10-19T19:53:15Z)
• Reinforced Data Sampling for Model Diversification [15.547681142342846]
  This paper proposes a new Reinforced Data Sampling (RDS) method to learn how to sample data adequately. We formulate the optimisation problem of model diversification $delta-div$ in data sampling to maximise learning potentials and optimum allocation by injecting model diversity. Our results suggest that the trainable sampling for model diversification is useful for competition organisers, researchers, or even starters to pursue full potentials of various machine learning tasks.
  arXiv  Detail & Related papers  (2020-06-12T11:46:13Z)

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.