Investigating Semi-Supervised Learning Algorithms in Text Datasets

• URL: http://arxiv.org/abs/2401.01843v2
• Date: Sun, 7 Jan 2024 11:51:33 GMT
• Title: Investigating Semi-Supervised Learning Algorithms in Text Datasets
• Authors: Himmet Toprak Kesgin, Mehmet Fatih Amasyali
• Abstract summary: Using large training datasets enhances the generalization capabilities of neural networks. Semi-supervised learning (SSL) is useful when there are few labeled data and a lot of unlabeled data. In this study, we compared SSL algorithms that do not require augmentation; these are self-training, co-training, tri-training, and tri-training with disagreement.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Using large training datasets enhances the generalization capabilities of neural networks. Semi-supervised learning (SSL) is useful when there are few labeled data and a lot of unlabeled data. SSL methods that use data augmentation are most successful for image datasets. In contrast, texts do not have consistent augmentation methods as images. Consequently, methods that use augmentation are not as effective in text data as they are in image data. In this study, we compared SSL algorithms that do not require augmentation; these are self-training, co-training, tri-training, and tri-training with disagreement. In the experiments, we used 4 different text datasets for different tasks. We examined the algorithms from a variety of perspectives by asking experiment questions and suggested several improvements. Among the algorithms, tri-training with disagreement showed the closest performance to the Oracle; however, performance gap shows that new semi-supervised algorithms or improvements in existing methods are needed.

Related papers

• On Pretraining Data Diversity for Self-Supervised Learning [57.91495006862553]
  We explore the impact of training with more diverse datasets on the performance of self-supervised learning (SSL) under a fixed computational budget. Our findings consistently demonstrate that increasing pretraining data diversity enhances SSL performance, albeit only when the distribution distance to the downstream data is minimal.
  arXiv  Detail & Related papers  (2024-03-20T17:59:58Z)
• Augmentations vs Algorithms: What Works in Self-Supervised Learning [9.194402355758164]
  We study the relative effects of data augmentations, pretraining algorithms, and model architectures in Self-Supervised Learning (SSL) We propose a new framework which unifies many seemingly disparate SSL methods into a single shared template.
  arXiv  Detail & Related papers  (2024-03-08T23:42:06Z)
• Can semi-supervised learning use all the data effectively? A lower bound perspective [58.71657561857055]
  We show that semi-supervised learning algorithms can leverage unlabeled data to improve over the labeled sample complexity of supervised learning algorithms. Our work suggests that, while proving performance gains for SSL algorithms is possible, it requires careful tracking of constants.
  arXiv  Detail & Related papers  (2023-11-30T13:48:50Z)
• TrueDeep: A systematic approach of crack detection with less data [0.0]
  We show that by incorporating domain knowledge along with deep learning architectures, we can achieve similar performance with less data. Our algorithms, developed with 23% of the overall data, have a similar performance on the test data and significantly better performance on multiple blind datasets.
  arXiv  Detail & Related papers  (2023-05-30T14:51:58Z)
• Evolutionary Augmentation Policy Optimization for Self-supervised Learning [10.087678954934155]
  Self-supervised learning is a machine learning algorithm for pretraining Deep Neural Networks (DNNs) without requiring manually labeled data. In this paper, we study the contribution of augmentation operators on the performance of self supervised learning algorithms.
  arXiv  Detail & Related papers  (2023-03-02T21:16:53Z)
• A Survey on Self-supervised Learning: Algorithms, Applications, and Future Trends [82.64268080902742]
  Self-supervised learning (SSL) aims to learn discriminative features from unlabeled data without relying on human-annotated labels. SSL has garnered significant attention recently, leading to the development of numerous related algorithms. This paper presents a review of diverse SSL methods, encompassing algorithmic aspects, application domains, three key trends, and open research questions.
  arXiv  Detail & Related papers  (2023-01-13T14:41:05Z)
• Collaborative Intelligence Orchestration: Inconsistency-Based Fusion of Semi-Supervised Learning and Active Learning [60.26659373318915]
  Active learning (AL) and semi-supervised learning (SSL) are two effective, but often isolated, means to alleviate the data-hungry problem. We propose an innovative Inconsistency-based virtual aDvErial algorithm to further investigate SSL-AL's potential superiority. Two real-world case studies visualize the practical industrial value of applying and deploying the proposed data sampling algorithm.
  arXiv  Detail & Related papers  (2022-06-07T13:28:43Z)
• Open-Set Semi-Supervised Learning for 3D Point Cloud Understanding [62.17020485045456]
  It is commonly assumed in semi-supervised learning (SSL) that the unlabeled data are drawn from the same distribution as that of the labeled ones. We propose to selectively utilize unlabeled data through sample weighting, so that only conducive unlabeled data would be prioritized.
  arXiv  Detail & Related papers  (2022-05-02T16:09:17Z)
• Evaluating BERT-based Pre-training Language Models for Detecting Misinformation [2.1915057426589746]
  It is challenging to control the quality of online information due to the lack of supervision over all the information posted online. There is a need for automated rumour detection techniques to limit the adverse effects of spreading misinformation. This study proposes the BERT-based pre-trained language models to encode text data into vectors and utilise neural network models to classify these vectors to detect misinformation.
  arXiv  Detail & Related papers  (2022-03-15T08:54:36Z)
• Feature transforms for image data augmentation [74.12025519234153]
  In image classification, many augmentation approaches utilize simple image manipulation algorithms. In this work, we build ensembles on the data level by adding images generated by combining fourteen augmentation approaches. Pretrained ResNet50 networks are finetuned on training sets that include images derived from each augmentation method.
  arXiv  Detail & Related papers  (2022-01-24T14:12:29Z)
• AugNet: End-to-End Unsupervised Visual Representation Learning with Image Augmentation [3.6790362352712873]
  We propose AugNet, a new deep learning training paradigm to learn image features from a collection of unlabeled pictures. Our experiments demonstrate that the method is able to represent the image in low dimensional space. Unlike many deep-learning-based image retrieval algorithms, our approach does not require access to external annotated datasets.
  arXiv  Detail & Related papers  (2021-06-11T09:02:30Z)

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.