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Semi and Weakly Supervised Semantic Segmentation Using Generative Adversarial Network
[article]
2017
arXiv
pre-print
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In particular, we propose a semi-supervised framework ,based on Generative Adversarial Networks (GANs), which consists of a generator network to provide extra training examples to a multi-class classifier ...
To address this lack, in this paper, we leverage, on one hand, massive amount of available unlabeled or weakly labeled data, and on the other hand, non-real images created through Generative Adversarial ...
Weakly Supervised Learning using Conditional Generative Adversarial Networks An recent extension of GANs is conditional GANs [16] , where generator and discriminator are provided with extra information ...
arXiv:1703.09695v1
fatcat:zsti7tl5c5cgbo4rptplla74f4
A Survey on Semi-Supervised Semantic Segmentation
[article]
2023
arXiv
pre-print
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on the results obtained, the challenges and the most promising lines of future research. ...
In this scenario, it makes sense to approach the problem from a semi-supervised point of view, where both labeled and unlabeled images are exploited. ...
the project with reference SOMM17/6110/ UGR, granted by the Andalusian ''Consejería de Conocimiento, Investigaci ón y Universidades" and European Regional Development Funds (ERDF). ...
arXiv:2302.09899v1
fatcat:ppc4vyybtfdnnonzlftdy2y26a
Weakly-Supervised Domain Adaptation with Adversarial Entropy for Building Segmentation in Cross-Domain Aerial Imagery
2021
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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This approach has achieved remarkable performance based on a fully convolutional network with adequate pixel-wise annotations. ...
In this article, we propose a weakly-supervised domain adaptation method using adversarial entropy for building segmentation to address this problem. ...
ACKNOWLEDGMENT The authors would like to thank the ISPRS Commission WG II/4, OSM and Google Maps for providing datasets as well as the editors and reviewers for providing valuable suggestions that refined ...
doi:10.1109/jstars.2021.3105421
fatcat:7nbctah4m5am5bmbcif6ehmb7m
Adversarial Learning for Semi-Supervised Semantic Segmentation
[article]
2018
arXiv
pre-print
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We propose a method for semi-supervised semantic segmentation using an adversarial network. ...
Experimental results on the PASCAL VOC 2012 and Cityscapes datasets demonstrate the effectiveness of the proposed algorithm. ...
Hung is supported in part by the NSF CAREER Grant #1149783, gifts from Adobe and NVIDIA. ...
arXiv:1802.07934v2
fatcat:vzcurbdrgzdfflikr6sdjup6um
Unsupervised Domain Adaptation for Semantic Segmentation of Urban Scenes
2019
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
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The proposed learning strategy exploits three components: a standard supervised learning on synthetic data, an adversarial learning strategy able to exploit both labeled synthetic data and unlabeled real ...
Experimental results prove the effectiveness of the proposed strategy in adapting a segmentation network trained on synthetic datasets, like GTA5 and SYNTHIA, to a real dataset as Cityscapes. ...
Similarly to the baseline approach, the adversarial loss L s G,2 is unable to adapt the network to the real domain, indeed the class road remains very badly detected also after its usage. ...
doi:10.1109/cvprw.2019.00160
dblp:conf/cvpr/BiasettonMAZ19
fatcat:xk7rtf5nprbozaq6ehs5dj4cpe
Adversarial Learning and Self-Teaching Techniques for Domain Adaptation in Semantic Segmentation
[article]
2020
arXiv
pre-print
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The proposed learning strategy is driven by three components: a standard supervised learning loss on labeled synthetic data; an adversarial learning module that exploits both labeled synthetic data and ...
Experimental results prove the effectiveness of the proposed strategy in adapting a segmentation network trained on synthetic datasets, like GTA5 and SYNTHIA, to real world datasets like Cityscapes and ...
between real and synthetic domain distributions mainly using generative models based on Generative Adversarial Networks (GANs) [35] - [39] . ...
arXiv:1909.00781v2
fatcat:zuzxvgac2bc4jlglx5qbhkcs2m
A Survey of Visual Sensory Anomaly Detection
[article]
2022
arXiv
pre-print
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In this survey, we are the first one to provide a comprehensive review of visual sensory AD and category into three levels according to the form of anomalies. ...
Furthermore, we classify each kind of anomaly according to the level of supervision. Finally, we summarize the challenges and provide open directions for this community. ...
Generally speaking, the research on scene-level AD mainly focuses on road or autonomous driving scenarios, and AD in other scenarios is also an interesting task. ...
arXiv:2202.07006v1
fatcat:2bqzmmrnjzggti5tcewa3mh3sa
$\mathrm{BAS}^4$Net: Boundary-Aware Semi-Supervised Semantic Segmentation Network for Very High Resolution Remote Sensing Images
2020
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Additionally, to decrease the amount of difficult and tedious manual labeling of remote sensing images, a discriminator network infers pseudolabels from unlabeled images to assist semi-supervised learning ...
and further improves the performance of the segmentation network. ...
Citation information: DOI 10.1109/JSTARS.2020.3021098, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ...
doi:10.1109/jstars.2020.3021098
fatcat:unvmqs3he5fxzfxpjoxn44gjxi
Anomaly Detection for Agricultural Vehicles Using Autoencoders
2022
Sensors
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compared with a baseline object detector based on YOLOv5. ...
Current vision-based algorithms for object detection and classification are unable to detect unknown classes of objects. ...
[16] , an exiting semantic segmentation algorithm generates the semantic map. Then, the approach utilizes generative adversarial network (GAN) to generate the resynthesized image. ...
doi:10.3390/s22103608
pmid:35632017
pmcid:PMC9145690
fatcat:oqny6sojobeajaf5kj4xpuxgqy
SemiSANet: A Semi-Supervised High-Resolution Remote Sensing Image Change Detection Model Using Siamese Networks with Graph Attention
2022
Remote Sensing
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To address this limitation, a simple semi-supervised change detection method based on consistency regularization and strong augmentation is proposed in this paper. ...
Change detection (CD) is one of the important applications of remote sensing and plays an important role in disaster assessment, land use detection, and urban sprawl tracking. ...
[20] proposed a semisupervised CD method based on generative adversarial networks (GAN). ...
doi:10.3390/rs14122801
fatcat:4b44tresqbhhdnqot4rhtxihii
Features to Text: A Comprehensive Survey of Deep Learning on Semantic Segmentation and Image Captioning
2021
Complexity
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In this paper, semantic segmentation and image captioning are comprehensively investigated based on traditional and state-of-the-art methodologies. ...
In this survey, we deliberate on the use of deep learning techniques on the segmentation analysis of both 2D and 3D images using a fully convolutional network and other high-level hierarchical feature ...
Weakly Supervised and Semisupervised Approaches. ough most models depend on a large number of images and their annotated label, the process of manually annotating labels is quite daunting and time-consuming ...
doi:10.1155/2021/5538927
fatcat:4yae4kjqdne6vaqus5plna4mwm
Semi-Supervised Semantic Segmentation in Earth Observation: The MiniFrance Suite, Dataset Analysis and Multi-task Network Study
[article]
2020
arXiv
pre-print
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Finally, we present semi-supervised deep architectures based on multi-task learning and the first experiments on MiniFrance. ...
and generalizes well in a semi-supervised setting. ...
The authors acknowledge the IGN for providing the BD ORTHO database under Open Licence v1.0 (https://www.etalab.gouv.fr/ licence-ouverte-open-licence) and the European Copernicus Program for providing ...
arXiv:2010.07830v1
fatcat:h7k5dnh5nnhffl2iy4o6yg67du
Defect Detection Methods for Industrial Products Using Deep Learning Techniques: A Review
2023
Algorithms
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First, deep learning-based detection of surface defects on industrial products is discussed from three perspectives: supervised, semi-supervised, and unsupervised. ...
This review paper aims to briefly summarize and analyze the current state of research on detecting defects using machine learning methods. ...
A semisupervised model of convolutional autoencoder (CAE) and generative adversarial network is proposed in [55] . ...
doi:10.3390/a16020095
fatcat:kir4zf6qlzfjbd6wva4mbxastq
Adversarial Learning based Discriminative Domain Adaptation for Geospatial Image Analysis
2021
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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based domain adaptation. ...
First, we approached the problem of unavailable target domain labels with unsupervised domain adaptation and then extended our method for semi-supervised domain adaptation to use a few available labels ...
A weakly supervised learning approach used in [34] and [25] proposed a predicted probability maps based approach for semi-supervised domain adaptation. ...
doi:10.1109/jstars.2021.3132259
fatcat:5ppi25cwirc2bmnlgolauiwga4
Transfer Adaptation Learning: A Decade Survey
[article]
2020
arXiv
pre-print
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and adversarial adaptation, which are beyond the early semi-supervised and unsupervised split. ...
Conventional machine learning aims to find a model with the minimum expected risk on test data by minimizing the regularized empirical risk on the training data, which, however, supposes that the training ...
ACKNOWLEDGMENT The author would like to thank the pioneer researchers in transfer learning, domain adaptation and other related fields. The author would also like to thank Dr. Mingsheng Long and Dr. ...
arXiv:1903.04687v2
fatcat:wurprqieffalnnp6isfkhh5y5i
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