Spanning Training Progress: Temporal Dual-Depth Scoring (TDDS) for Enhanced Dataset Pruning

• URL: http://arxiv.org/abs/2311.13613v3
• Date: Tue, 28 May 2024 05:34:47 GMT
• Title: Spanning Training Progress: Temporal Dual-Depth Scoring (TDDS) for Enhanced Dataset Pruning
• Authors: Xin Zhang, Jiawei Du, Yunsong Li, Weiying Xie, Joey Tianyi Zhou,
• Abstract summary: We propose a novel dataset pruning method termed as Temporal Dual-Depth Scoring (TDDS) Our method achieves 54.51% accuracy with only 10% training data, surpassing random selection by 7.83% and other comparison methods by at least 12.69%.
• Score: 50.809769498312434
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Dataset pruning aims to construct a coreset capable of achieving performance comparable to the original, full dataset. Most existing dataset pruning methods rely on snapshot-based criteria to identify representative samples, often resulting in poor generalization across various pruning and cross-architecture scenarios. Recent studies have addressed this issue by expanding the scope of training dynamics considered, including factors such as forgetting event and probability change, typically using an averaging approach. However, these works struggle to integrate a broader range of training dynamics without overlooking well-generalized samples, which may not be sufficiently highlighted in an averaging manner. In this study, we propose a novel dataset pruning method termed as Temporal Dual-Depth Scoring (TDDS), to tackle this problem. TDDS utilizes a dual-depth strategy to achieve a balance between incorporating extensive training dynamics and identifying representative samples for dataset pruning. In the first depth, we estimate the series of each sample's individual contributions spanning the training progress, ensuring comprehensive integration of training dynamics. In the second depth, we focus on the variability of the sample-wise contributions identified in the first depth to highlight well-generalized samples. Extensive experiments conducted on CIFAR and ImageNet datasets verify the superiority of TDDS over previous SOTA methods. Specifically on CIFAR-100, our method achieves 54.51% accuracy with only 10% training data, surpassing random selection by 7.83% and other comparison methods by at least 12.69%.

Related papers

• Distribution-Aware Robust Learning from Long-Tailed Data with Noisy Labels [8.14255560923536]
  Real-world data often exhibit long-tailed distributions and label noise, significantly degrading generalization performance. Recent studies have focused on noisy sample selection methods that estimate the centroid of each class based on high-confidence samples within each target class. We present Distribution-aware Sample Selection and Contrastive Learning (DaSC) to generate enhanced class centroids.
  arXiv  Detail & Related papers  (2024-07-23T19:06:15Z)
• Probing Perfection: The Relentless Art of Meddling for Pulmonary Airway Segmentation from HRCT via a Human-AI Collaboration Based Active Learning Method [13.384578466263566]
  In pulmonary tracheal segmentation, the scarcity of annotated data is a prevalent issue. Deep Learning (DL) methods face challenges: the opacity of 'black box' models and the need for performance enhancement. We address these challenges by combining diverse query strategies with various DL models.
  arXiv  Detail & Related papers  (2024-07-03T23:27:53Z)
• Take the Bull by the Horns: Hard Sample-Reweighted Continual Training Improves LLM Generalization [165.98557106089777]
  A key challenge is to enhance the capabilities of large language models (LLMs) amid a looming shortage of high-quality training data. Our study starts from an empirical strategy for the light continual training of LLMs using their original pre-training data sets. We then formalize this strategy into a principled framework of Instance-Reweighted Distributionally Robust Optimization.
  arXiv  Detail & Related papers  (2024-02-22T04:10:57Z)
• Finding Foundation Models for Time Series Classification with a PreText Task [7.197233473373693]
  This paper introduces pre-trained domain foundation models for Time Series Classification. A key aspect of our methodology is a novel pretext task that spans multiple datasets. Our experiments on the UCR archive demonstrate that this pre-training strategy significantly outperforms the conventional training approach without pre-training.
  arXiv  Detail & Related papers  (2023-11-24T15:03:55Z)
• Data Pruning via Moving-one-Sample-out [61.45441981346064]
  We propose a novel data-pruning approach called moving-one-sample-out (MoSo) MoSo aims to identify and remove the least informative samples from the training set. Experimental results demonstrate that MoSo effectively mitigates severe performance degradation at high pruning ratios.
  arXiv  Detail & Related papers  (2023-10-23T08:00:03Z)
• D2 Pruning: Message Passing for Balancing Diversity and Difficulty in Data Pruning [70.98091101459421]
  Coreset selection seeks to select a subset of the training data so as to maximize the performance of models trained on this subset, also referred to as coreset. We propose a novel pruning algorithm, D2 Pruning, that uses forward and reverse message passing over this dataset graph for coreset selection. Results show that D2 Pruning improves coreset selection over previous state-of-the-art methods for up to 70% pruning rates.
  arXiv  Detail & Related papers  (2023-10-11T23:01:29Z)
• Repeated Random Sampling for Minimizing the Time-to-Accuracy of Learning [28.042568086423298]
  Repeated Sampling of Random Subsets (RS2) is a powerful yet overlooked random sampling strategy. We test RS2 against thirty state-of-the-art data pruning and data distillation methods across four datasets including ImageNet. Our results demonstrate that RS2 significantly reduces time-to-accuracy compared to existing techniques.
  arXiv  Detail & Related papers  (2023-05-28T20:38:13Z)
• Dataset Pruning: Reducing Training Data by Examining Generalization Influence [30.30255670341501]
  Do all training data contribute to model's performance? How to construct a smallest subset from the entire training data as a proxy training set without significantly sacrificing the model's performance?
  arXiv  Detail & Related papers  (2022-05-19T05:36:35Z)
• Jo-SRC: A Contrastive Approach for Combating Noisy Labels [58.867237220886885]
  We propose a noise-robust approach named Jo-SRC (Joint Sample Selection and Model Regularization based on Consistency) Specifically, we train the network in a contrastive learning manner. Predictions from two different views of each sample are used to estimate its "likelihood" of being clean or out-of-distribution.
  arXiv  Detail & Related papers  (2021-03-24T07:26:07Z)
• Omni-supervised Facial Expression Recognition via Distilled Data [120.11782405714234]
  We propose omni-supervised learning to exploit reliable samples in a large amount of unlabeled data for network training. We experimentally verify that the new dataset can significantly improve the ability of the learned FER model. To tackle this, we propose to apply a dataset distillation strategy to compress the created dataset into several informative class-wise images.
  arXiv  Detail & Related papers  (2020-05-18T09:36:51Z)

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.