Published on Thu Aug 27 2020

Moderately supervised learning: definition and framework

Yongquan Yang, Zhongxi Zheng

Supervised learning (SL) has achieved remarkable success in numerous artificial intelligence applications. FSL is roughly categorized as fully supervised learning (FSL) and weakly supervised learning (WSL) However, solutions for various FSL tasks have shown that the given ground-truth labels are not always learnable.

0
0
0
Abstract

Supervised learning (SL) has achieved remarkable success in numerous artificial intelligence applications. In the current literature, by referring to the properties of the ground-truth labels prepared for a training data set, SL is roughly categorized as fully supervised learning (FSL) and weakly supervised learning (WSL). However, solutions for various FSL tasks have shown that the given ground-truth labels are not always learnable, and the target transformation from the given ground-truth labels to learnable targets can significantly affect the performance of the final FSL solutions. Without considering the properties of the target transformation from the given ground-truth labels to learnable targets, the roughness of the FSL category conceals some details that can be critical to building the optimal solutions for some specific FSL tasks. Thus, it is desirable to reveal these details. This article attempts to achieve this goal by expanding the categorization of FSL and investigating the subtype that plays the central role in FSL. Taking into consideration the properties of the target transformation from the given ground-truth labels to learnable targets, we first categorize FSL into three narrower subtypes. Then, we focus on the subtype moderately supervised learning (MSL). MSL concerns the situation where the given ground-truth labels are ideal, but due to the simplicity in annotation of the given ground-truth labels, careful designs are required to transform the given ground-truth labels into learnable targets. From the perspectives of definition and framework, we comprehensively illustrate MSL to reveal what details are concealed by the roughness of the FSL category. Finally, discussions on the revealed details suggest that MSL should be given more attention.

Sun Jul 05 2020
Machine Learning
Meta-Semi: A Meta-learning Approach for Semi-supervised Learning
Deep learning based semi-supervised learning (SSL) algorithms have led to promising results in recent years. However, they tend to introduce multiple tunable hyper-parameters, making them less practical in real SSL scenarios. In this paper, we propose a novel meta-learning based SSL algorithm (
0
0
0
Thu Dec 13 2018
Computer Vision
When Semi-Supervised Learning Meets Transfer Learning: Training Strategies, Models and Datasets
Semi-Supervised Learning (SSL) has been proved to be an effective way toverage both labeled and unlabeled data at the same time. The gains from SSL techniques over a fully-supervised baseline are smaller when trained from a pre-trained model.
0
0
0
Tue May 28 2019
Machine Learning
A Review of Semi Supervised Learning Theories and Recent Advances
Semi-supervised learning has been applied successfully in many fields. It is a new type of learning method between traditional supervised learning and unsupervised learning.
0
0
0
Tue Jun 22 2021
Machine Learning
Recent Deep Semi-supervised Learning Approaches and Related Works
The author of this work proposes an overview of the recent semi-supervised learning approaches and related works. Despite the remarkable success of neural networks in various applications, there exist few formidable constraints including the need for a large amount of labeled data.
0
0
0
Tue Jun 09 2020
Machine Learning
An Overview of Deep Semi-Supervised Learning
Deep neural networks demonstrated their ability to provide remarkable performances on a wide range of supervised learning tasks. However, creating such large datasets requires a considerable amount of resources, time, and effort. In a search for more data-efficient deep learning methods to overcome the need for large annotated
0
0
0
Mon Mar 15 2021
Machine Learning
Semi-supervised learning by selective training with pseudo labels via confidence estimation
0
0
0