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Deep Domain Adaptation under Deep Label Scarcity
[article]
2018
arXiv
pre-print
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The goal behind Domain Adaptation (DA) is to leverage the labeled examples from a source domain so as to infer an accurate model in a target domain where labels are not available or in scarce at the best ...
This approach requires a large number of labeled examples from the source domain to be able to infer a good model for the target domain. ...
The field of domain adaptation (DA) aims at easing out learner's job under such stress situations by allowing a transfer of learned models to other domain that faces label scarcity or absence. ...
arXiv:1809.08097v1
fatcat:hqdy6xfdojfgxpe5mj6r2v3s7m
MT-UDA: Towards Unsupervised Cross-modality Medical Image Segmentation with Limited Source Labels
[chapter]
2021
Lecture Notes in Computer Science
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2022 pre-print arXiv:2203.12454v1 fatcat:njmclusbdvgxxdmbtyeav54ngi
Although deep unsupervised domain adaptation (UDA) can leverage well-established source domain annotations and abundant target domain data to facilitate cross-modality image segmentation and also mitigate ...
We evaluate our method on MM-WHS 2017 dataset and demonstrate that our approach outperforms the state-of-the-art methods by a large margin under the source-label scarcity scenario. ...
beneath D s s to improve the segmentation performance on D s under label scarcity. ...
doi:10.1007/978-3-030-87193-2_28
fatcat:cmqrhlvgpvba5kape4qawz5eti
Research on fault diagnosis method of deep transfer learning driven by simulation data
2022
Vibroengineering PROCEDIA
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Sufficient labeled fault samples are the key to ensuring the performance of deep learning diagnostic models. ...
The deep learning diagnosis model trained directly with simulation data lacks versatility and cannot be applied to fault diagnosis of real data. ...
[11] proposed a deep transfer learning method based on sub-domain adaptation, by introducing sub-domain adaptation and adversarial learning, while aligning the local and global feature distributions ...
doi:10.21595/vp.2022.22674
fatcat:ama6rxd445gyhb4fqhen6dhev4
Dual-Component Deep Domain Adaptation: A New Approach for Cross Project Software Vulnerability Detection
[chapter]
2020
Lecture Notes in Computer Science
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One possible solution is to employ deep domain adaptation (DA) which has recently witnessed enormous success in transferring learning from structural labeled to unlabeled data sources. ...
Our aim in this paper is to propose Dual Generator-Discriminator Deep Code Domain Adaptation Network (Dual-GD-DDAN) for tackling the problem of transfer learning from labeled to unlabeled software projects ...
This research was supported under the Defence Science and Technology Group's Next Generation Technologies Program. ...
doi:10.1007/978-3-030-47426-3_54
fatcat:ibdlwish7bcqnkefisvt3kkbo4
Optimization of Rural Smart Tourism Service Model with Internet of Things
2022
Security and Communication Networks
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When it comes to training, however, there exist source domain data sets that are completely labeled and can be used as a supplement to the target domain training data sets. (2) A personalized recommendation ...
The main research contents of the paper are as follows: (1) A new algorithm for recommending tourism destinations with domain adaptability has been suggested. ...
To put it another way, domain adaptation can address the issue of using labeled samples from the source domain data set to help classify data from the target domain when the distribution of data between ...
doi:10.1155/2022/6004728
fatcat:dzcdkoqjlbbrbk2t3gigsxnjgu
Advances in Deep Learning for Hyperspectral Image Analysis—Addressing Challenges Arising in Practical Imaging Scenarios
[chapter]
2020
Advances in Computer Vision and Pattern Recognition
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2020 pre-print arXiv:2007.08592v1 fatcat:7lrxy7e52fhdhnqk5g44x25wru
Challenges include limited ground truth (annotation is expensive and extensive labeling is often not feasible), and high dimensional nature of the data (each pixel is represented by hundreds of spectral ...
In recent years, there has been an emergence of deep learning algorithms being applied to hyperspectral and multispectral imagery for remote sensing and biomedicine tasks. ...
Transfer learning and Domain Adaptation Effective training has always been a challenge with deep learning models. ...
doi:10.1007/978-3-030-38617-7_5
fatcat:23ibk4ojbvepbpikxgjxan4i6e
Revisiting Vicinal Risk Minimization for Partially Supervised Multi-Label Classification Under Data Scarcity
[article]
2022
arXiv
pre-print
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The empirical results also provide insights into future research directions on partially supervised learning under data scarcity. ...
This paper offers an empirical understanding of an under-explored problem, namely partially supervised multi-label classification (PSMLC), where a multi-label classifier is trained with only partially ...
This is the first study of partially supervised multilabel classification (PSMLC) under data scarcity in the medical domain. 2. ...
arXiv:2204.08954v1
fatcat:63qpvfrhuzctjp23ova6zm4e4y
Transfer Adaptation Learning: A Decade Survey
[article]
2020
arXiv
pre-print
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Broader solutions of transfer adaptation learning being created by researchers are identified, i.e., instance re-weighting adaptation, feature adaptation, classifier adaptation, deep network adaptation ...
A research problem is characterized as transfer adaptation learning (TAL) when it needs knowledge correspondence between different moments/domains. ...
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
Neural Unsupervised Domain Adaptation in NLP—A Survey
[article]
2020
arXiv
pre-print
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Motivated by the latest advances, in this survey we review neural unsupervised domain adaptation techniques which do not require labeled target domain data. ...
In contrast, learning from unlabeled data, especially under domain shift, remains a challenge. ...
For many target applications, labeled data is lacking (Y scarcity), and even for pre-training general models data might be scarce (X scarcity). ...
arXiv:2006.00632v2
fatcat:n4g3yelofzdqpoacta3agjahde
A Cross-City Federated Transfer Learning Framework: A Case Study on Urban Region Profiling
[article]
2022
arXiv
pre-print
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., data missing and label scarcity) caused by inadequate services and infrastructures or imbalanced development levels of cities have seriously affected the urban computing tasks in real scenarios. ...
With our adaptation of federated training and homomorphic encryption settings, CcFTL can effectively deal with the data privacy problem among cities. ...
: We further explore the effectiveness of our model CcFTL under varying proportions of label scarcity in the target city. ...
arXiv:2206.00007v3
fatcat:kcuqefmq2zbyrifij6sknonapi
Meta learning to classify intent and slot labels with noisy few shot examples
[article]
2020
arXiv
pre-print
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Recently deep learning has dominated many machine learning areas, including spoken language understanding (SLU). ...
We establish the task by defining few-shot splits on three public IC/SL datasets, ATIS, SNIPS, and TOP, and adding two types of natural noises (adaptation example missing/replacing and modality mismatch ...
Transfer learning usually refers to pre-training initial models using mismatched domains with rich human annotations and then adapting the models with limited labels in targeted domains. ...
arXiv:2012.07516v1
fatcat:hrsgnmpf4fevvh2kdefvircymi
A Real Use Case of Semi-Supervised Learning for Mammogram Classification in a Local Clinic of Costa Rica
[article]
2021
arXiv
pre-print
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The scarcity of labeled data can also bring a challenge towards the application of transfer learning with models trained using these source datasets. ...
It is shown that the use of semi-supervised deep learning combined with fine-tuning can provide a meaningful advantage when using scarce labeled observations. ...
This work is partially supported by by the Ministry of Science, Innovation and Universities of Spain under grant number RTI2018-094645-B- ...
arXiv:2107.11696v1
fatcat:6jajcqzggreyxogav4sh7q6uym
Locality Preserving Joint Transfer for Domain Adaptation
[article]
2019
arXiv
pre-print
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Domain adaptation aims to leverage knowledge from a well-labeled source domain to a poorly-labeled target domain. ...
Notably, our approach is suitable for both homogeneous and heterogeneous domain adaptation by learning domain-specific projections. ...
INTRODUCTION T HE scarcity principle 1 is one of the commonsense principles in our daily life. In the field of machine learning, the scarcity shows up in labeled samples. ...
arXiv:1906.07441v1
fatcat:2ontem74c5cvlo2qditieesx2u
Mining Label Distribution Drift in Unsupervised Domain Adaptation
[article]
2020
arXiv
pre-print
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Numerical results and empirical model analysis show that LMDAN delivers superior performance compared to other state-of-the-art domain adaptation methods under such scenarios. ...
Finally, different from general domain adaptation experiments, we modify domain adaptation datasets to create the considerable label distribution drift between source and target domain. ...
Among these settings, unsupervised domain adaptation, containing no label but only samples in target domain, is a challenging but practical one, owing to actual scenes are suffering from label-scarcity ...
arXiv:2006.09565v1
fatcat:pkpzkxeiijhqfas3h23zjh5rcy
Sparsely-Labeled Source Assisted Domain Adaptation
[article]
2020
arXiv
pre-print
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This paper proposes a novel Sparsely-Labeled Source Assisted Domain Adaptation (SLSA-DA) algorithm to address the challenge with limited labeled source domain samples. ...
Domain Adaptation (DA) aims to generalize the classifier learned from the source domain to the target domain. ...
Label quality and its sufficiency are both important in context of domain adaptation (DA), especially for deep learning DA frameworks [7] . ...
arXiv:2005.04111v1
fatcat:hka66nryzjd6pdatrsev6fleam
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