Research and Implementation of Data Enhancement Techniques for Graph Neural Networks

• URL: http://arxiv.org/abs/2406.12640v1
• Date: Tue, 18 Jun 2024 14:07:38 GMT
• Title: Research and Implementation of Data Enhancement Techniques for Graph Neural Networks
• Authors: Jingzhao Gu, Haoyang Huang,
• Abstract summary: In practical engineering applications, some data are affected by the conditions under which more data cannot be obtained or the cost of obtaining data is too high. This paper firstly analyses the key points of the data enhancement technology of graph neural network, and at the same time introduces the composition of graph neural network in depth.
• Score: 10.575426305555538
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Data, algorithms, and arithmetic power are the three foundational conditions for deep learning to be effective in the application domain. Data is the focus for developing deep learning algorithms. In practical engineering applications, some data are affected by the conditions under which more data cannot be obtained or the cost of obtaining data is too high, resulting in smaller data sets (generally several hundred to several thousand) and data sizes that are far smaller than the size of large data sets (tens of thousands). The above two methods are based on the original dataset to generate, in the case of insufficient data volume of the original data may not reflect all the real environment, such as the real environment of the light, silhouette and other information, if the amount of data is not enough, it is difficult to use a simple transformation or neural network generative model to generate the required data. The research in this paper firstly analyses the key points of the data enhancement technology of graph neural network, and at the same time introduces the composition foundation of graph neural network in depth, on the basis of which the data enhancement technology of graph neural network is optimized and analysed.

Related papers

• Data Augmentations in Deep Weight Spaces [89.45272760013928]
  We introduce a novel augmentation scheme based on the Mixup method. We evaluate the performance of these techniques on existing benchmarks as well as new benchmarks we generate.
  arXiv  Detail & Related papers  (2023-11-15T10:43:13Z)
• Dataset Quantization [72.61936019738076]
  We present dataset quantization (DQ), a new framework to compress large-scale datasets into small subsets. DQ is the first method that can successfully distill large-scale datasets such as ImageNet-1k with a state-of-the-art compression ratio.
  arXiv  Detail & Related papers  (2023-08-21T07:24:29Z)
• A Data-Centric Approach for Training Deep Neural Networks with Less Data [1.9014535120129343]
  This paper summarizes our winning submission to the "Data-Centric AI" competition. We discuss some of the challenges that arise while training with a small dataset. We propose a GAN-based solution for synthesizing new data points.
  arXiv  Detail & Related papers  (2021-10-07T16:41:52Z)
• Transformer Networks for Data Augmentation of Human Physical Activity Recognition [61.303828551910634]
  State of the art models like Recurrent Generative Adrial Networks (RGAN) are used to generate realistic synthetic data. In this paper, transformer based generative adversarial networks which have global attention on data, are compared on PAMAP2 and Real World Human Activity Recognition data sets with RGAN.
  arXiv  Detail & Related papers  (2021-09-02T16:47:29Z)
• On the Pitfalls of Learning with Limited Data: A Facial Expression Recognition Case Study [0.5249805590164901]
  We focus on the problem of Facial Expression Recognition from videos. We performed an extensive study with four databases at a different complexity and nine deep-learning architectures for video classification. We found that complex training sets translate better to more stable test sets when trained with transfer learning and synthetically generated data.
  arXiv  Detail & Related papers  (2021-04-02T18:53:41Z)
• Complex Wavelet SSIM based Image Data Augmentation [0.0]
  We look at the MNIST handwritten dataset an image dataset used for digit recognition. We take a detailed look into one of the most popular augmentation techniques used for this data set elastic deformation. We propose to use a similarity measure called Complex Wavelet Structural Similarity Index Measure (CWSSIM) to selectively filter out the irrelevant data.
  arXiv  Detail & Related papers  (2020-07-11T21:11:46Z)
• Dataset Condensation with Gradient Matching [36.14340188365505]
  We propose a training set synthesis technique for data-efficient learning, called dataset Condensation, that learns to condense large dataset into a small set of informative synthetic samples for training deep neural networks from scratch. We rigorously evaluate its performance in several computer vision benchmarks and demonstrate that it significantly outperforms the state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-06-10T16:30:52Z)
• Data Consistent CT Reconstruction from Insufficient Data with Learned Prior Images [70.13735569016752]
  We investigate the robustness of deep learning in CT image reconstruction by showing false negative and false positive lesion cases. We propose a data consistent reconstruction (DCR) method to improve their image quality, which combines the advantages of compressed sensing and deep learning. The efficacy of the proposed method is demonstrated in cone-beam CT with truncated data, limited-angle data and sparse-view data, respectively.
  arXiv  Detail & Related papers  (2020-05-20T13:30:49Z)
• Supervised Learning on Relational Databases with Graph Neural Networks [10.279748604797911]
  Training machine learning models on data stored in relational databases requires significant data extraction and feature engineering efforts. We introduce a method that uses Graph Neural Networks to overcome these challenges. Our proposed method outperforms state-of-the-art automatic feature engineering methods on two out of three datasets.
  arXiv  Detail & Related papers  (2020-02-06T00:57:39Z)
• Neural Data Server: A Large-Scale Search Engine for Transfer Learning Data [78.74367441804183]
  We introduce Neural Data Server (NDS), a large-scale search engine for finding the most useful transfer learning data to the target domain. NDS consists of a dataserver which indexes several large popular image datasets, and aims to recommend data to a client. We show the effectiveness of NDS in various transfer learning scenarios, demonstrating state-of-the-art performance on several target datasets.
  arXiv  Detail & Related papers  (2020-01-09T01:21:30Z)
• DeGAN : Data-Enriching GAN for Retrieving Representative Samples from a Trained Classifier [58.979104709647295]
  We bridge the gap between the abundance of available data and lack of relevant data, for the future learning tasks of a trained network. We use the available data, that may be an imbalanced subset of the original training dataset, or a related domain dataset, to retrieve representative samples. We demonstrate that data from a related domain can be leveraged to achieve state-of-the-art performance.
  arXiv  Detail & Related papers  (2019-12-27T02:05:45Z)

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.