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Domain Adaptation: A Survey

https://doi.org/10.1007/978-981-19-7867-8_47
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27/27 checkable references clean · checked 2026-08-09

Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.

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The 27 checked references that resolve
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Direct domain adaptation through reciprocal linear transformations
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Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks
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Partial Transfer Learning with Selective Adversarial Networks
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Optimal Transport for Domain Adaptation
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DeepJDOT: Deep Joint Distribution Optimal Transport for Unsupervised Domain Adaptation
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Domain Generalization for Object Recognition with Multi-task Autoencoders
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Deep Reconstruction-Classification Networks for Unsupervised Domain Adaptation
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Transferable approach for cardiac disease classification using deep learning
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Learning Transferable Parameters for Unsupervised Domain Adaptation
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Category Contrast for Unsupervised Domain Adaptation in Visual Tasks
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Gradient-based learning applied to document recognition
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Performance Assessment Framework for Computational Models of Visual Attention
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K-nearest neighbor
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On Minimum Discrepancy Estimation for Deep Domain Adaptation
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Adapting Visual Category Models to New Domains
resolves10.1109/ICSSIT46314.2019.8987942
Object Detection and Count of Objects in Image using Tensor Flow Object Detection API
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Return of Frustratingly Easy Domain Adaptation
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Deep CORAL: Correlation Alignment for Deep Domain Adaptation
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Deep Learning Model for Image Classification
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Machine Learning and Metaheuristics Algorithms, and Applications
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Adversarial Discriminative Domain Adaptation
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Deep Hashing Network for Unsupervised Domain Adaptation
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Cross-Domain Contrastive Learning for Unsupervised Domain Adaptation
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Dynamic Weighted Learning for Unsupervised Domain Adaptation
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Mind the Class Weight Bias: Weighted Maximum Mean Discrepancy for Unsupervised Domain Adaptation
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The Domain Shift Problem of Medical Image Segmentation and Vendor-Adaptation by Unet-GAN
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Online Unsupervised Domain Adaptation via Reducing Inter- and Intra-Domain Discrepancies
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no DOI — not checkedArjovsky, M., Chintala, S., Bottou, L.: Wasserstein generative adversarial networks. In International Conference on Machine Learning, pp. 214–223. PMLR (2017)
no DOI — not checkedAshokkumar, P., Don, S.: High dimensional data visualization: a survey. J. Adv. Res. Dyn. Control Syst. 9(12), 851–866 (2017)
no DOI — not checkedBousmalis, K., Trigeorgis, G., Silberman, N., Krishnan, D., Erhan, D.: Domain separation networks. Adv. Neural Inform. Process. Syst. 29 (2016)
no DOI — not checkedGanin, Y., Lempitsky, V.: Unsupervised domain adaptation by backpropagation. In: International Conference on Machine Learning, pp. 1180–1189. PMLR (2015)
no DOI — not checkedGanin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., Lempitsky, V.: Domain-adversarial training of neural networks. J. Mach. Learn. Res. 17(1), 2096–2030 (2016)
no DOI — not checkedGressel, G., Hrudya, P., Surendran, K., Thara, S., Aravind, A., Prabaharan, P.: Ensemble learning approach for author profiling. In: Notebook for PAN at CLEF, pp. 401–412 (2014)
no DOI — not checkedGriffin, G., Holub, A., Perona, P.: Caltech-256 Object Category Dataset (2007)
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no DOI — not checkedLong, M., Cao, Y., Wang, J., Jordan, M.: Learning transferable features with deep adaptation networks. In: International Conference on Machine Learning, pp. 97–105. PMLR (2015)
no DOI — not checkedLong, M., Zhu, H., Wang, J., Jordan, M.I.: Unsupervised Domain Adaptation with Residual Transfer Networks (2016). arXiv preprint arXiv:1602.04433
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