GDeR: Safeguarding Efficiency, Balancing, and Robustness via Prototypical Graph Pruning

• URL: http://arxiv.org/abs/2410.13761v1
• Date: Thu, 17 Oct 2024 16:56:01 GMT
• Title: GDeR: Safeguarding Efficiency, Balancing, and Robustness via Prototypical Graph Pruning
• Authors: Guibin Zhang, Haonan Dong, Yuchen Zhang, Zhixun Li, Dingshuo Chen, Kai Wang, Tianlong Chen, Yuxuan Liang, Dawei Cheng, Kun Wang,
• Abstract summary: We introduce a novel soft-pruning method, GDeR, designed to update the training during the process using trainable prototypes. GDeR achieves or surpasses the performance of the full dataset with 30%50% fewer training samples. It also outperforms state-of-the-art pruning methods in imbalanced training and noisy training scenarios.
• Score: 44.401418612374286
• License:
• Abstract: Training high-quality deep models necessitates vast amounts of data, resulting in overwhelming computational and memory demands. Recently, data pruning, distillation, and coreset selection have been developed to streamline data volume by retaining, synthesizing, or selecting a small yet informative subset from the full set. Among these methods, data pruning incurs the least additional training cost and offers the most practical acceleration benefits. However, it is the most vulnerable, often suffering significant performance degradation with imbalanced or biased data schema, thus raising concerns about its accuracy and reliability in on-device deployment. Therefore, there is a looming need for a new data pruning paradigm that maintains the efficiency of previous practices while ensuring balance and robustness. Unlike the fields of computer vision and natural language processing, where mature solutions have been developed to address these issues, graph neural networks (GNNs) continue to struggle with increasingly large-scale, imbalanced, and noisy datasets, lacking a unified dataset pruning solution. To achieve this, we introduce a novel dynamic soft-pruning method, GDeR, designed to update the training ``basket'' during the process using trainable prototypes. GDeR first constructs a well-modeled graph embedding hypersphere and then samples \textit{representative, balanced, and unbiased subsets} from this embedding space, which achieves the goal we called Graph Training Debugging. Extensive experiments on five datasets across three GNN backbones, demonstrate that GDeR (I) achieves or surpasses the performance of the full dataset with 30%~50% fewer training samples, (II) attains up to a 2.81x lossless training speedup, and (III) outperforms state-of-the-art pruning methods in imbalanced training and noisy training scenarios by 0.3%~4.3% and 3.6%~7.8%, respectively.

Related papers

• NUDGE: Lightweight Non-Parametric Fine-Tuning of Embeddings for Retrieval [0.7646713951724011]
  Existing approaches either fine-tune the pre-trained model itself or, more efficiently, train adaptor models to transform the output of the pre-trained model. We present NUDGE, a family of novel non-parametric embedding fine-tuning approaches. NUDGE directly modifies the embeddings of data records to maximize the accuracy of $k$-NN retrieval.
  arXiv  Detail & Related papers  (2024-09-04T00:10:36Z)
• Just How Flexible are Neural Networks in Practice? [89.80474583606242]
  It is widely believed that a neural network can fit a training set containing at least as many samples as it has parameters. In practice, however, we only find solutions via our training procedure, including the gradient and regularizers, limiting flexibility.
  arXiv  Detail & Related papers  (2024-06-17T12:24:45Z)
• Effective pruning of web-scale datasets based on complexity of concept clusters [48.125618324485195]
  We present a method for pruning large-scale multimodal datasets for training CLIP-style models on ImageNet. We find that training on a smaller set of high-quality data can lead to higher performance with significantly lower training costs. We achieve a new state-of-the-art Imagehttps://info.arxiv.org/help/prep#commentsNet zero-shot accuracy and a competitive average zero-shot accuracy on 38 evaluation tasks.
  arXiv  Detail & Related papers  (2024-01-09T14:32:24Z)
• Efficient Grammatical Error Correction Via Multi-Task Training and Optimized Training Schedule [55.08778142798106]
  We propose auxiliary tasks that exploit the alignment between the original and corrected sentences. We formulate each task as a sequence-to-sequence problem and perform multi-task training. We find that the order of datasets used for training and even individual instances within a dataset may have important effects on the final performance.
  arXiv  Detail & Related papers  (2023-11-20T14:50:12Z)
• Robust Learning with Progressive Data Expansion Against Spurious Correlation [65.83104529677234]
  We study the learning process of a two-layer nonlinear convolutional neural network in the presence of spurious features. Our analysis suggests that imbalanced data groups and easily learnable spurious features can lead to the dominance of spurious features during the learning process. We propose a new training algorithm called PDE that efficiently enhances the model's robustness for a better worst-group performance.
  arXiv  Detail & Related papers  (2023-06-08T05:44:06Z)
• 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)
• Boosting Visual-Language Models by Exploiting Hard Samples [126.35125029639168]
  HELIP is a cost-effective strategy tailored to enhance the performance of existing CLIP models. Our method allows for effortless integration with existing models' training pipelines. On comprehensive benchmarks, HELIP consistently boosts existing models to achieve leading performance.
  arXiv  Detail & Related papers  (2023-05-09T07:00:17Z)
• Diving into Unified Data-Model Sparsity for Class-Imbalanced Graph Representation Learning [30.23894624193583]
  Graph Neural Networks (GNNs) training upon non-Euclidean graph data often encounters relatively higher time costs. We develop a unified data-model dynamic sparsity framework named Graph Decantation (GraphDec) to address challenges brought by training upon a massive class-imbalanced graph data.
  arXiv  Detail & Related papers  (2022-10-01T01:47:00Z)
• Boosting Facial Expression Recognition by A Semi-Supervised Progressive Teacher [54.50747989860957]
  We propose a semi-supervised learning algorithm named Progressive Teacher (PT) to utilize reliable FER datasets as well as large-scale unlabeled expression images for effective training. Experiments on widely-used databases RAF-DB and FERPlus validate the effectiveness of our method, which achieves state-of-the-art performance with accuracy of 89.57% on RAF-DB.
  arXiv  Detail & Related papers  (2022-05-28T07:47:53Z)
• 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)

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.