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Semi-supervised Deep Learning with Memory [chapter]

Yanbei Chen, Xiatian Zhu, Shaogang Gong
2018 Lecture Notes in Computer Science  
In this work, we propose a novel Memory-Assisted Deep Neural Network (MA-DNN) capable of exploiting the memory of model learning to enable semi-supervised learning.  ...  To address this problem, existing semi-supervised deep learning methods often rely on the up-to-date "network-in-training" to formulate the semi-supervised learning objective.  ...  Illustration of the memory-assisted semi-supervised deep learning framework that integrates a deep CNN with an external memory module trained concurrently.  ... 
doi:10.1007/978-3-030-01246-5_17 fatcat:xxd7ckagrfhtjdyvveqf4fabiu

A review of various semi-supervised learning models with a deep learning and memory approach

Jamshid Bagherzadeh, Hasan Asil
2018 Iran Journal of Computer Science  
The aim of this paper was to analyze the available models of semi-supervised learning with an approach to deep learning.  ...  Various models of this method have been presented to deal with semi-supervised data such as deep generative, virtual adversarial, and Ladder models.  ...  Table 4 indicates semi-supervised models based on deep learning.  ... 
doi:10.1007/s42044-018-00027-6 fatcat:nccifurxyzc33fa5xfprrlupxq

Deep Semi-supervised Metric Learning with Dual Alignment for Cervical Cancer Cell Detection [article]

Zhizhong Chai, Luyang Luo, Huangjing Lin, Hao Chen, Anjia Han, Pheng-Ann Heng
2022 arXiv   pre-print
Extensive experiments show our proposed method outperforms other state-of-the-art semi-supervised approaches consistently, demonstrating the efficacy of our proposed deep semi-supervised metric learning  ...  In this paper, we propose a novel deep semi-supervised metric learning method to effectively leverage both labeled and unlabeled data for cervical cancer cell detection.  ...  To enable large-scale semi-supervised cervical cancer cell detection, we present a novel deep semi-supervised metric learning network.  ... 
arXiv:2104.03265v2 fatcat:kzchmda7u5bmpg42oqbf3ywl3e

Semi-Supervised Training in Deep Learning Acoustic Model

Yan Huang, Yongqiang Wang, Yifan Gong
2016 Interspeech 2016  
We studied the semi-supervised training in a fully connected deep neural network (DNN), unfolded recurrent neural network (RNN), and long short-term memory recurrent neural network (LSTM-RNN) with respect  ...  The semi-supervised LSTM-RNN yields 6.56% relative WER reduction against the supervised baseline.  ...  This is especially true with the emerging new types of deep learning acoustic model with ever enlarged model capacity.  ... 
doi:10.21437/interspeech.2016-1596 dblp:conf/interspeech/HuangWG16 fatcat:tj6j6oxy7nh4llidsaapbxcmkq

EVALUATING DEEP SEMI-SUPERVISED LEARNING METHODS FOR COMPUTER VISION APPLICATIONS

Habib Ullah, Mohib Ullah, Sultan Daud Khan, Faouzi Alaya Cheikh
2021 IS&T International Symposium on Electronic Imaging Science and Technology  
Deep semi-supervised learning (SSL) have been significantly investigated in the past few years due to its broad spectrum of theory, algorithms, and applications.  ...  [16] introduced a novel memory-assisted deep neural network capable of using the memory of model learning to enable semi-supervised learning.  ...  [15] proposed a semi-supervised deep learning method, using temporal ensembling of deep long short-term memory, to recognize human activities with smartphone inertial sensors.  ... 
doi:10.2352/issn.2470-1173.2021.6.iriacv-313 fatcat:m6s2vuwr6fepbm5qab53r47fpu

Quantum Semi-Supervised Learning with Quantum Supremacy [article]

Zhou Shangnan
2022 arXiv   pre-print
Moreover, we provide a protocol in systematically designing quantum machine learning algorithms with quantum supremacy, which can be extended beyond quantum semi-supervised learning.  ...  We propose a novel framework that resolves both issues: quantum semi-supervised learning.  ...  Hence, in some way, semi-supervised learning is more generic, and its algorithms can be used in supervised or unsupervised learning with small modifications.  ... 
arXiv:2110.02343v3 fatcat:v5xnywljurcsvgkzxfkrbkpsvm

Adaptive Learning Knowledge Networks for Few-Shot Learning

Minghao Yan
2019 IEEE Access  
Considering the situations of standard few-shot learning and semi-supervised few-shot learning, we design different update strategies for the memory of learned knowledge.  ...  In recent years, relying on training with thousands of labeled samples, deep learning has achieved remarkable success in the field of computer vision.  ...  Our model is also applicable to the semi-supervised few-shot learning. We design a new update strategy for the memory of knowledge.  ... 
doi:10.1109/access.2019.2934694 fatcat:lathc27e4ffu3joj56kq3mrmna

Dealing with Scarce Labelled Data: Semi-supervised Deep Learning with Mix Match for Covid-19 Detection Using Chest X-ray Images

Saul Calderon-Ramirez, Raghvendra Giri, Shengxiang Yang, Armaghan Moemeni, Mario Umana, David Elizondo, Jordina Torrents-Barrena, Miguel A. Molina-Cabello
2021 2020 25th International Conference on Pattern Recognition (ICPR)  
To overcome this challenge, a semi-supervised deep learning model using both labelled and unlabelled data is proposed.  ...  Also, we introduce a semi-supervised deep learning boost coefficient which is meant to ease the scalability of our approach and performance comparison.  ...  Semi-supervised deep learning Semi-supervised deep learning is an increasingly popular approach to deal with scarcely labelled datasets.  ... 
doi:10.1109/icpr48806.2021.9412946 fatcat:grddkxr5onb6ppgnv4dzlbgpka

Deep Machine Learning and Neural Networks: An Overview

Chandrahas Mishra, D. L. Gupta
2017 IAES International Journal of Artificial Intelligence (IJ-AI)  
In this paper brief introduction to all machine learning paradigm and application area of deep machine learning and different types of neural networks with applications is discussed.  ...  Deep learning is a technique of machine learning in artificial intelligence area.  ...  Semi-Supervised Learning The semi-supervised learning paradigm is of special significance in both theory and applications.  ... 
doi:10.11591/ijai.v6.i2.pp66-73 fatcat:4j3ljjw4ebbxbaj4egpmupco6e

Adversarial Semi-supervised Learning for Corporate Credit Ratings [article]

Bojing Feng, Wenfang Xue
2021 arXiv   pre-print
Then in the second phase, adversarial semi-supervised learning is applied uniting labeled data and pseudo-labeled data.  ...  Specifically, we consider the problem of adversarial semi-supervised learning (ASSL) for corporate credit rating which has been rarely researched before.  ...  Now in deep learning, it begins with Muti-Layer Perception (MLP), going through Convolution Neural Networks (CNN) to Long-Short-Term-Memory (LSTM).  ... 
arXiv:2104.02479v2 fatcat:hgnqbyc5krabtnrfnw2kvlirme

TIME SERIES LAND COVER CLASSIFICATION BASED ON SEMI-SUPERVISED CONVOLUTIONAL LONG SHORT-TERM MEMORY NEURAL NETWORKS

S. Jing, T. Chao
2020 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
In this study, we proposed a semi-supervised convolutional long short-term memory neural network (Semi-LSTM) in long time series which achieves an accurate and automated land cover classification with  ...  With the plenty accumulation of historical images, the inclusion of time series data becomes available to utilize, but it is difficult to avoid missing values caused by cloud cover.  ...  Learning temporal features via the semi-supervised ConvLSTM model In this section, we detail the semi-supervised ConvLSTM model and how to deal with the classification task of time series images.  ... 
doi:10.5194/isprs-archives-xliii-b2-2020-1521-2020 fatcat:627kr3oqljburct452wrxpdk5m

Detecting 11K Classes: Large Scale Object Detection Without Fine-Grained Bounding Boxes

Hao Yang, Hao Wu, Hao Chen
2019 2019 IEEE/CVF International Conference on Computer Vision (ICCV)  
Recent advances in deep learning greatly boost the performance of object detection.  ...  We achieve this by utilizing the correlations between coarse-grained and fine-grained classes with shared backbone, soft-attention based proposal reranking, and a dual-level memory module.  ...  WSDDN [2] is one of the well-known work for weakly-supervised object detection with deep learning.  ... 
doi:10.1109/iccv.2019.00990 dblp:conf/iccv/YangWC19 fatcat:euiavla6uvfs3id5sobuul6v5y

Deep Machine Learning and Neural Networks: An Overview

Chandrahas Mishra, Dharmendra Lal Gupta
2016 International Journal of Hybrid Information Technology  
In this paper thorough survey to all machine learning paradigms and application areas of deep machine learning and different types of neural networks with applications are discussed.  ...  Deep learning is a technique of machine learning in artificial intelligence area.  ...  This is because supervised learning is reasonably well understood and unsupervised learning does not directly aim at predicting outputs from inputs. 4.Semi-Supervised Learning The semi-supervised learning  ... 
doi:10.14257/ijhit.2016.9.11.34 fatcat:tvbfxbm5pnea5bjsjy5w7t3jem

Semisupervised Deep Reinforcement Learning in Support of IoT and Smart City Services

Mehdi Mohammadi, Ala Al-Fuqaha, Mohsen Guizani, Jun-Seok Oh
2018 IEEE Internet of Things Journal  
To the best of our knowledge, the proposed model is the first investigation that extends deep reinforcement learning to the semi-supervised paradigm.  ...  In this paper, we propose a semi-supervised deep reinforcement learning model that fits smart city applications as it consumes both labeled and unlabeled data to improve the performance and accuracy of  ...  In [31] , deep learning joined with semi-supervised learning as well as extreme learning machine are applied to unlabeled data to study the performance of feature extraction and classification phases  ... 
doi:10.1109/jiot.2017.2712560 fatcat:hxs2ghbf65e3rnnuhsqbhly2eq

Supervised and Semi-supervised Deep Probabilistic Models for Indoor Positioning Problems [article]

Weizhu Qian, Fabrice Lauri, Franck Gechter
2019 arXiv   pre-print
In this work, we propose two novel deep learning-based models, the convolutional mixture density recurrent neural network and the VAE-based semi-supervised learning model.  ...  Further, since most of real-world datasets are not labeled, we devise the VAE-based model for the semi-supervised learning tasks.  ...  models to perform semi-supervised learning for accurate indoor positioning. • We conduct the evaluation experiments on the realworld datasets and compare our methods with other deep learning methods.  ... 
arXiv:1911.09906v2 fatcat:i4g4mrwe4ndyfd6uhd6t275u3e
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