Recent Deep Semi-supervised Learning Approaches and Related Works
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 a few formidable constraints, including the need for a large amount of labeled data. Therefore, semi-supervised learning, which is a learning scheme in which scarce labels and a larger amount of unlabeled data are utilized to train models (e.g., deep neural networks), is getting more important. Based on the key assumptions of semi-supervised learning, which are the manifold assumption, cluster assumption, and continuity assumption, the work reviews the recent semi-supervised learning approaches. In particular, the methods in regard to using deep neural networks in a semi-supervised learning setting are primarily discussed. In addition, the existing works are first classified based on the underlying idea and explained, then the holistic approaches that unify the aforementioned ideas are detailed.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
LLM-guided Semi-Supervised Approaches for Social Media Crisis Data Classification
Semi-supervised learning approaches have been investigated as a means to enhance the analysis of social media data in disaster management contexts. In this work, we present the first empirical evaluation of large languag…
Semi-Supervised Few-Shot Learning with Prototypical Random Walks
Recent progress has shown that few-shot learning can be improved with access to unlabelled data, known as semi-supervised few-shot learning(SS-FSL). We introduce an SS-FSL approach, dubbed as Prototypical Random Walk Net…
Few-Shot LearningSafe Semi-Supervised Learning of Sum-Product Networks
In several domains obtaining class annotations is expensive while at the same time unlabelled data are abundant. While most semi-supervised approaches enforce restrictive assumptions on the data distribution, recent work…
Diff-SySC: An Approach Using Diffusion Models for Semi-Supervised Image Classification
Diffusion models have revolutionized the field of generative machine learning due to their effectiveness in capturing complex, multimodal data distributions. Semi-supervised learning represents a technique that allows th…
image-classificationImage ClassificationSemi-Supervised Image ClassificationSemi-supervised Sequence-to-sequence ASR using Unpaired Speech and Text
Sequence-to-sequence automatic speech recognition (ASR) models require large quantities of data to attain high performance. For this reason, there has been a recent surge in interest for unsupervised and semi-supervised …
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Semi-Supervised Image Classificationspeech-recognition+3