Language Semantic Graph Guided Data-Efficient Learning

• URL: http://arxiv.org/abs/2311.08782v1
• Date: Wed, 15 Nov 2023 08:54:57 GMT
• Title: Language Semantic Graph Guided Data-Efficient Learning
• Authors: Wenxuan Ma and Shuang Li and Lincan Cai and Jingxuan Kang
• Abstract summary: We introduce a Language Semantic Graph (LSG) which is constructed from labels manifest as natural language descriptions. An auxiliary graph neural network is trained to extract high-level semantic relations and then used to guide the training of the primary model. Our in-depth analysis shows that the LSG method also expedites the training process.
• Score: 10.039953846594805
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Developing generalizable models that can effectively learn from limited data and with minimal reliance on human supervision is a significant objective within the machine learning community, particularly in the era of deep neural networks. Therefore, to achieve data-efficient learning, researchers typically explore approaches that can leverage more related or unlabeled data without necessitating additional manual labeling efforts, such as Semi-Supervised Learning (SSL), Transfer Learning (TL), and Data Augmentation (DA). SSL leverages unlabeled data in the training process, while TL enables the transfer of expertise from related data distributions. DA broadens the dataset by synthesizing new data from existing examples. However, the significance of additional knowledge contained within labels has been largely overlooked in research. In this paper, we propose a novel perspective on data efficiency that involves exploiting the semantic information contained in the labels of the available data. Specifically, we introduce a Language Semantic Graph (LSG) which is constructed from labels manifest as natural language descriptions. Upon this graph, an auxiliary graph neural network is trained to extract high-level semantic relations and then used to guide the training of the primary model, enabling more adequate utilization of label knowledge. Across image, video, and audio modalities, we utilize the LSG method in both TL and SSL scenarios and illustrate its versatility in significantly enhancing performance compared to other data-efficient learning approaches. Additionally, our in-depth analysis shows that the LSG method also expedites the training process.

Related papers

• Learning with Less: Knowledge Distillation from Large Language Models via Unlabeled Data [54.934578742209716]
  In real-world NLP applications, Large Language Models (LLMs) offer promising solutions due to their extensive training on vast datasets. LLKD is an adaptive sample selection method that incorporates signals from both the teacher and student. Our comprehensive experiments show that LLKD achieves superior performance across various datasets with higher data efficiency.
  arXiv  Detail & Related papers  (2024-11-12T18:57:59Z)
• Web-Scale Visual Entity Recognition: An LLM-Driven Data Approach [56.55633052479446]
  Web-scale visual entity recognition presents significant challenges due to the lack of clean, large-scale training data. We propose a novel methodology to curate such a dataset, leveraging a multimodal large language model (LLM) for label verification, metadata generation, and rationale explanation. Experiments demonstrate that models trained on this automatically curated data achieve state-of-the-art performance on web-scale visual entity recognition tasks.
  arXiv  Detail & Related papers  (2024-10-31T06:55:24Z)
• A Survey of the Self Supervised Learning Mechanisms for Vision Transformers [5.152455218955949]
  The application of self supervised learning (SSL) in vision tasks has gained significant attention. We develop a comprehensive taxonomy of systematically classifying the SSL techniques. We discuss the motivations behind SSL, review popular pre-training tasks, and highlight the challenges and advancements in this field.
  arXiv  Detail & Related papers  (2024-08-30T07:38:28Z)
• Exploiting the Semantic Knowledge of Pre-trained Text-Encoders for Continual Learning [70.64617500380287]
  Continual learning allows models to learn from new data while retaining previously learned knowledge. The semantic knowledge available in the label information of the images, offers important semantic information that can be related with previously acquired knowledge of semantic classes. We propose integrating semantic guidance within and across tasks by capturing semantic similarity using text embeddings.
  arXiv  Detail & Related papers  (2024-08-02T07:51:44Z)
• Does Decentralized Learning with Non-IID Unlabeled Data Benefit from Self Supervision? [51.00034621304361]
  We study decentralized learning with unlabeled data through the lens of self-supervised learning (SSL) We study the effectiveness of contrastive learning algorithms under decentralized learning settings.
  arXiv  Detail & Related papers  (2022-10-20T01:32:41Z)
• Self-supervised on Graphs: Contrastive, Generative,or Predictive [25.679620842010422]
  Self-supervised learning (SSL) is emerging as a new paradigm for extracting informative knowledge through well-designed pretext tasks. We divide existing graph SSL methods into three categories: contrastive, generative, and predictive. We also summarize the commonly used datasets, evaluation metrics, downstream tasks, and open-source implementations of various algorithms.
  arXiv  Detail & Related papers  (2021-05-16T03:30:03Z)
• Graph-based Semi-supervised Learning: A Comprehensive Review [51.26862262550445]
  Semi-supervised learning (SSL) has tremendous value in practice due to its ability to utilize both labeled data and unlabelled data. An important class of SSL methods is to naturally represent data as graphs, which corresponds to graph-based semi-supervised learning (GSSL) methods. GSSL methods have demonstrated their advantages in various domains due to their uniqueness of structure, the universality of applications, and their scalability to large scale data.
  arXiv  Detail & Related papers  (2021-02-26T05:11:09Z)
• Contrastive and Generative Graph Convolutional Networks for Graph-based Semi-Supervised Learning [64.98816284854067]
  Graph-based Semi-Supervised Learning (SSL) aims to transfer the labels of a handful of labeled data to the remaining massive unlabeled data via a graph. A novel GCN-based SSL algorithm is presented in this paper to enrich the supervision signals by utilizing both data similarities and graph structure.
  arXiv  Detail & Related papers  (2020-09-15T13:59:28Z)

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.