--1 CONTENTS

By A. Bolte, Asadollah Mataji, Ghavomoddin Zahedi Amiri. 2006

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Evaluation & Benchmarks

Citations in the corpus

Data-Efficient Image Recognition with Contrastive Predictive Coding (2019, background): “mly. Other approaches to semi-supervised learning train simultaneously on labeled and unlabeled data. Many such works have roots in early work on bootstrapping [57] and co-training [6] from text (see [71] for a survey). An increasingly popular approach in deep learning consists in propagating inform”
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence (2020, neutral): “Broader introductions are provided in [60, 61, 6].”
Semi-Supervised QA with Generative Domain-Adaptive Nets (2017, background): “Semi-supervised learning has been extensively studied in literature (Zhu, 2005).”
Semi-supervised Learning with Deep Generative Models (2014, background): “Existing generative approaches based on models such as Gaussian mixture or hidden Markov models (Zhu, 2006), have not been very successful due to the need for a large number of mixtures components or states to perform well.”
Temporal Ensembling for Semi-Supervised Learning (2016, background): “There is a large body of previous work on semi-supervised learning (Zhu, 2005).”
Meta-Learning for Semi-Supervised Few-Shot Classification (2018, background): “As for the literature on semi-supervised learning, while it is quite vast (Zhu, 2005; Chapelle et al., 2010), the most relevant category to our work is related to self-training (Yarowsky, 1995; Rosenberg et al., 2005).”
A survey of data fusion in smart city applications (2019, background): “Note that, semi-supervised machine learning approach [74] is also categorized under this class.”
A Survey on Data Collection for Machine Learning: A Big Data - AI Integration Perspective (2018, neutral): “Semi-supervised learning techniques [143] exploit”
Semi-supervised learning, causality, and the conditional cluster assumption (2019, methodology): “One such method is semi-supervised learning (SSL) (Chapelle et al., 2010; Zhu, 2005) where, in addition to labelled data, possibly large numbers of unlabelled observations are available to the learner at training time.”
Masked Label Prediction: Unified Massage Passing Model for Semi-Supervised Classification (2020, background): “, 2019) and the Label Propagation Algorithms (LPAs) (Zhu, 2005; Zhu et al., 2003; Zhang & Lee, 2007; Wang & Zhang, 2007; Karasuyama & Mamitsuka, 2013; Gong et al., 2016; Liu et al., 2019).”
Transductive Multi-View Zero-Shot Learning (2015, background): “This novel graph structure better exploits the complementarity of different views in the common embedding space.”
Class-incremental Learning via Deep Model Consolidation (2019, background): “Semisupervised learning [56, 4] attempts to exploit the usefulness of unlabeled in-domain instances.”
Training Deep Neural Networks on Noisy Labels with Bootstrapping (2014, background): “The literature on semi-supervised and weakly-supervised learning is vast (see Zhu (2005) for a survey), and so in this section we focus on the key previous papers that inspired this work and on other papers on weakly-and semi-supervised deep learning.”
Data Distillation: Towards Omni-Supervised Learning (2017, background): “There is a great volume of work on semi-supervised learning, and comprehensive surveys can be found in [49, 4, 50].”
Learning from Noisy Large-Scale Datasets with Minimal Supervision (2017, background): “Second, semi-supervised learning (SSL) approaches tackle these shortcomings by combining the noisy labels with a small set of clean labels [33].”
Generalizing from a Few Examples (2019, background): “According to whether the oracle or human intervention is leveraged, this can be further classified into the following: – Semi-supervised learning [165], which learns from a small number of labeled samples and (usually a large number of) unlabeled samples in E.”
Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise (2018, background): “, 2015), and tenuously relates our work to the field of semi-supervised learning (Zhu, 2005; Chapelle et al., 2010).”
Smooth Neighbors on Teacher Graphs for Semi-Supervised Learning (2017, methodology): “Traditional models such as Gaussian mixtures [39] try to maximize the joint log-likelihood of both labeled and unlabeled data using an EM algorithm.”
ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation (2018, methodology): “Thus, it inspires several approaches for UDA, for example, self-training, generative model or class balancing [49].”