A Survey on Self-supervised Learning: Algorithms, Applications, and Future Trends
- URL: http://arxiv.org/abs/2301.05712v4
- Date: Sun, 14 Jul 2024 09:30:45 GMT
- Title: A Survey on Self-supervised Learning: Algorithms, Applications, and Future Trends
- Authors: Jie Gui, Tuo Chen, Jing Zhang, Qiong Cao, Zhenan Sun, Hao Luo, Dacheng Tao,
- Abstract summary: 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.
- Score: 82.64268080902742
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Deep supervised learning algorithms typically require a large volume of labeled data to achieve satisfactory performance. However, the process of collecting and labeling such data can be expensive and time-consuming. Self-supervised learning (SSL), a subset of unsupervised learning, 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. However, there is a dearth of comprehensive studies that elucidate the connections and evolution of different SSL variants. This paper presents a review of diverse SSL methods, encompassing algorithmic aspects, application domains, three key trends, and open research questions. Firstly, we provide a detailed introduction to the motivations behind most SSL algorithms and compare their commonalities and differences. Secondly, we explore representative applications of SSL in domains such as image processing, computer vision, and natural language processing. Lastly, we discuss the three primary trends observed in SSL research and highlight the open questions that remain. A curated collection of valuable resources can be accessed at https://github.com/guijiejie/SSL.
Related papers
- Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects [84.6945070729684]
Self-supervised learning (SSL) has recently achieved impressive performance on various time series tasks.
This article reviews current state-of-the-art SSL methods for time series data.
arXiv Detail & Related papers (2023-06-16T18:23:10Z) - Self-Supervised Learning for Point Clouds Data: A Survey [8.858165912687923]
Self-Supervised Learning (SSL) is considered as an essential solution to solve the time-consuming and labor-intensive data labelling problems.
This paper provides a comprehensive survey of recent advances on SSL for point clouds.
arXiv Detail & Related papers (2023-05-09T08:47:09Z) - Active Self-Supervised Learning: A Few Low-Cost Relationships Are All
You Need [34.013568381942775]
Self-Supervised Learning (SSL) has emerged as the solution of choice to learn transferable representations from unlabeled data.
In this work, we formalize and generalize this principle through Positive Active Learning (PAL) where an oracle queries semantic relationships between samples.
First, it unveils a theoretically grounded learning framework beyond SSL, based on similarity graphs, that can be extended to tackle supervised and semi-supervised learning depending on the employed oracle.
Second, it provides a consistent algorithm to embed a priori knowledge, e.g. some observed labels, into any SSL losses without any change in the training pipeline.
arXiv Detail & Related papers (2023-03-27T14:44:39Z) - OpenLDN: Learning to Discover Novel Classes for Open-World
Semi-Supervised Learning [110.40285771431687]
Semi-supervised learning (SSL) is one of the dominant approaches to address the annotation bottleneck of supervised learning.
Recent SSL methods can effectively leverage a large repository of unlabeled data to improve performance while relying on a small set of labeled data.
This work introduces OpenLDN that utilizes a pairwise similarity loss to discover novel classes.
arXiv Detail & Related papers (2022-07-05T18:51:05Z) - DATA: Domain-Aware and Task-Aware Pre-training [94.62676913928831]
We present DATA, a simple yet effective NAS approach specialized for self-supervised learning (SSL)
Our method achieves promising results across a wide range of computation costs on downstream tasks, including image classification, object detection and semantic segmentation.
arXiv Detail & Related papers (2022-03-17T02:38:49Z) - 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) - Rethinking Self-Supervised Learning: Small is Beautiful [30.809693803413445]
We propose scaled-down self-supervised learning (S3L), which include 3 parts: small resolution, small architecture and small data.
On a diverse set of datasets, S3L achieves higher accuracy consistently with much less training cost when compared to previous SSL learning paradigm.
arXiv Detail & Related papers (2021-03-25T01:48:52Z) - 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) - Self-Supervised Learning of Graph Neural Networks: A Unified Review [50.71341657322391]
Self-supervised learning is emerging as a new paradigm for making use of large amounts of unlabeled samples.
We provide a unified review of different ways of training graph neural networks (GNNs) using SSL.
Our treatment of SSL methods for GNNs sheds light on the similarities and differences of various methods, setting the stage for developing new methods and algorithms.
arXiv Detail & Related papers (2021-02-22T03:43:45Z)
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.