Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-15T16:50:33.198803Z
Paper Citation Record · LEDGER
Domain Adaptation Techniques for Natural and Medical Image Classification
As of 22 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 0 inbound Pith citation observations for arXiv:2508.20537.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
50 of 50 outbound references displayed
- verified exact0
- verified fuzzy40
- unresolved10
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation d6435a91-13dc-4700-80e0-bf5290f1ca20 · outbound
This paper cites Unpaired,unsuperviseddomainadaptationassumesyourdomainsarealreadysimilar.
Domain Adaptation Techniques for Natural and Medical Image Classification Unpaired,unsuperviseddomainadaptationassumesyourdomainsarealreadysimilar
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:50:33.829248Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 2cd19d03-b4be-43ab-83ca-1b744de203c1 · outbound
This paper cites Simulations of common unsupervised domain adaptation algorithms for image classification.IEEE Transactions on Instrumentation and Measurement, 74:1–17, 2025.
Domain Adaptation Techniques for Natural and Medical Image Classification Simulations of common unsupervised domain adaptation algorithms for image classification.IEEE Transactions on Instrumentation and Measurement, 74:1–17, 2025
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:50:33.817511Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation ed43e863-4ec9-462c-9076-9f311588a403 · outbound
This paper cites Domain adaptation in remote sensing image classification: A survey.
Domain Adaptation Techniques for Natural and Medical Image Classification Domain adaptation in remote sensing image classification: A survey
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:50:33.804910Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation dbe38c19-7312-4cdd-9371-b4df022bf3cd · outbound
This paper cites Correlation alignment for unsupervised domain adaptation.Domain adaptation in computer vision applications, pages 153–171, 2017.
Domain Adaptation Techniques for Natural and Medical Image Classification Correlation alignment for unsupervised domain adaptation.Domain adaptation in computer vision applications, pages 153–171, 2017
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:50:33.792323Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 1d89ffdb-6ff6-4eff-a780-854c1cde1419 · outbound
This paper cites Deep subdomain adaptation network for image classification.IEEE transactions on neural networks and learning systems, 32(4):1713–1722, 2020.
Domain Adaptation Techniques for Natural and Medical Image Classification Deep subdomain adaptation network for image classification.IEEE transactions on neural networks and learning systems, 32(4):1713–1722, 2020
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:50:33.779596Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation bfbc3962-6551-4677-bb53-730a20660b57 · outbound
This paper cites Unsuperviseddomainadaptationviadeepconditionaladaptationnetwork.
Domain Adaptation Techniques for Natural and Medical Image Classification Unsuperviseddomainadaptationviadeepconditionaladaptationnetwork
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:50:33.766944Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 5e380ba7-cb43-4ab1-8c1f-883863ce740d · outbound
This paper cites Deep CORAL: Correlation alignment for deep domain adaptation.
Domain Adaptation Techniques for Natural and Medical Image Classification Deep CORAL: Correlation alignment for deep domain adaptation
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:50:33.754677Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 06abc1ab-9678-4efd-a8d0-cdf96af82fff · outbound
This paper cites Heterogeneousdomainadaptationviacorrelativeanddiscriminativefeature learning.
Domain Adaptation Techniques for Natural and Medical Image Classification Heterogeneousdomainadaptationviacorrelativeanddiscriminativefeature learning
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:50:33.742195Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 3733ca79-9f7b-4d81-afa2-9cd57bf63bf8 · outbound
This paper cites Weakly correlated multimodal domain adaptation for pattern classification.
Domain Adaptation Techniques for Natural and Medical Image Classification Weakly correlated multimodal domain adaptation for pattern classification
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:50:33.730129Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation bb8fbad7-38de-426b-8c31-acd8243ded80 · outbound
This paper cites Domain-adversarial training of neural networks.The journal of machine learning research, 17(1):2096–2030, 2016.
Domain Adaptation Techniques for Natural and Medical Image Classification Domain-adversarial training of neural networks.The journal of machine learning research, 17(1):2096–2030, 2016
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:50:33.718106Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 42d7270a-1eb1-4145-a6d5-4e46cb558ce7 · outbound
This paper cites Reusing the task-specific classifier as a discriminator: Discriminator-free adversarial domain adaptation.
Domain Adaptation Techniques for Natural and Medical Image Classification Reusing the task-specific classifier as a discriminator: Discriminator-free adversarial domain adaptation
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:50:33.706506Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation c93eb15c-936c-41e5-8e1f-07ec2d339186 · outbound
This paper cites InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 3941–3950, 2020.
Domain Adaptation Techniques for Natural and Medical Image Classification InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 3941–3950, 2020
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:50:33.693960Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation bd62a78a-36ed-4c09-a628-11d56dd90702 · outbound
This paper cites Faa-clip:Federatedadversarialadaptationofclip.
Domain Adaptation Techniques for Natural and Medical Image Classification Faa-clip:Federatedadversarialadaptationofclip
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:50:33.681344Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation c0463437-b125-4c6a-ad4d-0e7abe265b6f · outbound
This paper cites Domain-guided conditional diffusion model for unsupervised domain adaptation.Neural Networks, 184:107031, 2025.
Domain Adaptation Techniques for Natural and Medical Image Classification Domain-guided conditional diffusion model for unsupervised domain adaptation.Neural Networks, 184:107031, 2025
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:50:33.667693Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 38e9a3ca-8e59-41c3-87ae-4c963b874372 · outbound
This paper cites Multi-source domain adaptation by causal-guided adaptive multimodal diffusion networks.International Journal of Computer Vision, 133(7):4623–4645, 2025.
Domain Adaptation Techniques for Natural and Medical Image Classification Multi-source domain adaptation by causal-guided adaptive multimodal diffusion networks.International Journal of Computer Vision, 133(7):4623–4645, 2025
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:50:33.655558Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 694c9b8e-f5c1-49a7-82bc-a9928cf55a39 · outbound
This paper cites Ddci: Unsupervised domain adaptation for remote sensing images based on diffusion causal distillation.IEEE Transactions on Geoscience and Remote Sensing, 63:1–12, 2025.
Domain Adaptation Techniques for Natural and Medical Image Classification Ddci: Unsupervised domain adaptation for remote sensing images based on diffusion causal distillation.IEEE Transactions on Geoscience and Remote Sensing, 63:1–12, 2025
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:50:33.643704Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 0c60a0d7-9259-49c4-be8b-dd06d658990f · outbound
This paper cites Textadapter: Self-supervised domain adaptation for cross-domain text recognition.IEEE Transactions on Multimedia, 26:9854–9865, 2024.
Domain Adaptation Techniques for Natural and Medical Image Classification Textadapter: Self-supervised domain adaptation for cross-domain text recognition.IEEE Transactions on Multimedia, 26:9854–9865, 2024
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:50:33.631837Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation d1d7f610-1db2-4039-8827-5bf45f3767ae · outbound
This paper cites Samda: Leveraging sam on few-shot domain adaptation for electronic microscopy segmentation.
Domain Adaptation Techniques for Natural and Medical Image Classification Samda: Leveraging sam on few-shot domain adaptation for electronic microscopy segmentation
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:50:33.618853Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation d03b1ad2-1d4e-4c91-a0cd-05ec22d89e39 · outbound
This paper cites Feature fusion transferability aware transformer for unsupervised domain adaptation.
Domain Adaptation Techniques for Natural and Medical Image Classification Feature fusion transferability aware transformer for unsupervised domain adaptation
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:50:33.605522Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation f500a9ce-a636-4959-b347-8fd7e2bff496 · outbound
This paper cites Robust unsupervised domain adaptation through negative-view regularization.
Domain Adaptation Techniques for Natural and Medical Image Classification Robust unsupervised domain adaptation through negative-view regularization
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:50:33.592337Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 9fd3bbbc-72c1-41a1-b607-70f335af93dd · outbound
This paper cites Open-set domain adaptation with visual-language foundation models.Computer Vision and Image Understanding, 250:104230, 2025.
Domain Adaptation Techniques for Natural and Medical Image Classification Open-set domain adaptation with visual-language foundation models.Computer Vision and Image Understanding, 250:104230, 2025
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:50:33.579063Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 4d1da1b5-ebc3-4812-b830-c74d6bd00b12 · outbound
This paper cites Adapting visual category models to new domains.
Domain Adaptation Techniques for Natural and Medical Image Classification Adapting visual category models to new domains
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:50:33.565746Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation a433d1ed-0d9b-4c34-b4f3-aecca541ff40 · outbound
This paper cites Reusing the task-specific classifier as a discriminator: Discriminator-freeadversarialdomainadaptation.
Domain Adaptation Techniques for Natural and Medical Image Classification Reusing the task-specific classifier as a discriminator: Discriminator-freeadversarialdomainadaptation
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:50:33.554033Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation ce8263e3-ceca-40d5-9fd8-c16de2390fc3 · outbound
This paper cites A brief review of domain adaptation.Advances in data science and information engineering, pages 877–894, 2021.
Domain Adaptation Techniques for Natural and Medical Image Classification A brief review of domain adaptation.Advances in data science and information engineering, pages 877–894, 2021
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:50:33.541389Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 7e8cb9b9-d5e5-49c9-82fa-c82f8d4e81f5 · outbound
This paper cites NeuralNetworks, page 106230, 2024.
Domain Adaptation Techniques for Natural and Medical Image Classification NeuralNetworks, page 106230, 2024
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:50:33.528102Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation a0274ada-ea08-42dd-aef6-271e0b2218bb · outbound
This paper cites Efficient unsupervised domain adaptation via self-supervised vision transformer and synergistic cross-domain alignment.
Domain Adaptation Techniques for Natural and Medical Image Classification Efficient unsupervised domain adaptation via self-supervised vision transformer and synergistic cross-domain alignment
Reference 26
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T16:50:33.104572Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b1277000-520a-4e98-a90f-cb56140ac5e1 · outbound
This paper cites Large-scale machine learning with stochastic gradient descent.
Domain Adaptation Techniques for Natural and Medical Image Classification Large-scale machine learning with stochastic gradient descent
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:50:33.514902Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 8fdc3337-f5eb-4c68-a290-a8d23e8a172e · outbound
This paper cites Visualizing data using t-SNE.Journal of machine learning research, 9(11), 2008.
Domain Adaptation Techniques for Natural and Medical Image Classification Visualizing data using t-SNE.Journal of machine learning research, 9(11), 2008
Reference 28
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T16:50:33.111790Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8bb8cf50-d025-487a-aa03-b1122adbaacc · outbound
This paper cites Analysis of representations for domain adaptation.Advances in neural information processing systems, 19, 2006.
Domain Adaptation Techniques for Natural and Medical Image Classification Analysis of representations for domain adaptation.Advances in neural information processing systems, 19, 2006
Reference 29
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T16:50:33.116009Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 63e1280d-2379-42bd-94a6-9f3e7af7b57d · outbound
This paper cites Grad-CAM: Visual explanations from deep networks via gradient-based localization.
Domain Adaptation Techniques for Natural and Medical Image Classification Grad-CAM: Visual explanations from deep networks via gradient-based localization
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:50:33.485749Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 6e79c3e0-4b0a-4af8-88a3-f7d22cdb5e0e · outbound
This paper cites Deep hashing network for unsupervised domain adaptation.
Domain Adaptation Techniques for Natural and Medical Image Classification Deep hashing network for unsupervised domain adaptation
Reference 31
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T16:50:33.124102Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5215ccbb-d456-431e-aff9-1aad4482d55c · outbound
This paper cites Themanyfacesofrobustness:Acriticalanalysisofout-of-distributiongeneralization.
Domain Adaptation Techniques for Natural and Medical Image Classification Themanyfacesofrobustness:Acriticalanalysisofout-of-distributiongeneralization
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:50:33.465448Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 37a26ed2-fb71-4a70-b8d4-55b912f218e0 · outbound
This paper cites Learning robust global representations by penalizing local predictive power.
Domain Adaptation Techniques for Natural and Medical Image Classification Learning robust global representations by penalizing local predictive power
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:50:33.453394Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 6bfc79c4-8aef-4b17-8bae-6c3006a927dc · outbound
This paper cites Adaptiope: A modern benchmark for unsupervised domain adaptation.
Domain Adaptation Techniques for Natural and Medical Image Classification Adaptiope: A modern benchmark for unsupervised domain adaptation
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:50:33.440412Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 9c401b20-f056-41d8-b118-9aa3e168a627 · outbound
This paper cites COVIDNet-CT: A tailored deep convolutional neural network design for detection of COVID-19 cases from chest CT images.Frontiers in Medicine, 7:1025, 2020.
Domain Adaptation Techniques for Natural and Medical Image Classification COVIDNet-CT: A tailored deep convolutional neural network design for detection of COVID-19 cases from chest CT images.Frontiers in Medicine, 7:1025, 2020
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:50:33.428393Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation e51a1349-fa3d-4285-99dc-ce1be20e0fed · outbound
This paper cites ChestX-ray8: Hospital-scale chest X-raydatabaseandbenchmarksonweakly-supervisedclassificationandlocalizationofcommonthoraxdiseases.
Domain Adaptation Techniques for Natural and Medical Image Classification ChestX-ray8: Hospital-scale chest X-raydatabaseandbenchmarksonweakly-supervisedclassificationandlocalizationofcommonthoraxdiseases
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:50:33.415878Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 3dc9a128-947b-4cf2-baef-938ae7fc7916 · outbound
This paper cites Vision transformer and explainabletransferlearningmodelsforautodetectionofkidneycyst,stoneandtumorfromCT-radiography.
Domain Adaptation Techniques for Natural and Medical Image Classification Vision transformer and explainabletransferlearningmodelsforautodetectionofkidneycyst,stoneandtumorfromCT-radiography
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:50:33.402641Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation e81ef36e-d976-42a5-9889-fb08b41ce68e · outbound
This paper cites Retinal fundus multi-disease image dataset (RFMiD): A dataset for multi-disease detection research.
Domain Adaptation Techniques for Natural and Medical Image Classification Retinal fundus multi-disease image dataset (RFMiD): A dataset for multi-disease detection research
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:50:33.389792Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation ee96ea80-23eb-4d05-814d-3915d53b4916 · outbound
This paper cites The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions.Scientific data, 5(1):1–9, 2018.
Domain Adaptation Techniques for Natural and Medical Image Classification The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions.Scientific data, 5(1):1–9, 2018
Reference 39
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T16:50:33.156541Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b2fc657d-7fc2-414d-bf91-1071bb9a66ec · outbound
This paper cites an unresolved cited work.
Domain Adaptation Techniques for Natural and Medical Image Classification Unresolved cited work
Reference 40
- Resolution
- unresolved
- raw_fallback, observed 2026-08-15T16:50:33.368439Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 42894100-2fb2-4440-acf5-c457ddb6f1f3 · outbound
This paper cites BCN20000: Dermoscopic Lesions in the Wild.
Domain Adaptation Techniques for Natural and Medical Image Classification BCN20000: Dermoscopic Lesions in the Wild
Reference 41
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T16:50:33.164488Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7b80841b-0e06-4cad-95be-ea6dee9b4e69 · outbound
This paper cites Deep residual learning for image recognition.
Domain Adaptation Techniques for Natural and Medical Image Classification Deep residual learning for image recognition
Reference 42
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T16:50:33.168920Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0a8d422e-1681-4071-9a7a-264673123e54 · outbound
This paper cites Densely connected convolutional networks.
Domain Adaptation Techniques for Natural and Medical Image Classification Densely connected convolutional networks
Reference 43
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T16:50:33.172745Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b580aa9d-026f-467a-9f0c-a309241a2f47 · outbound
This paper cites Shufflenet:Anextremelyefficientconvolutionalneuralnetworkformobiledevices.
Domain Adaptation Techniques for Natural and Medical Image Classification Shufflenet:Anextremelyefficientconvolutionalneuralnetworkformobiledevices
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:50:33.338218Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 0800f06b-1953-4ffe-9f7f-d89c7c8234ec · outbound
This paper cites MobileNetV2: Inverted residuals and linear bottlenecks.
Domain Adaptation Techniques for Natural and Medical Image Classification MobileNetV2: Inverted residuals and linear bottlenecks
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:50:33.323997Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 1b3f643e-4f0b-48ab-969a-e4b3b535c02d · outbound
This paper cites Multi-source and multi-target domain adaptation based on dynamic generator with attention.IEEE Transactions on Multimedia, 26:6891–6905, 2024.
Domain Adaptation Techniques for Natural and Medical Image Classification Multi-source and multi-target domain adaptation based on dynamic generator with attention.IEEE Transactions on Multimedia, 26:6891–6905, 2024
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:50:33.310134Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation bb294023-36be-4e5e-bbb7-2c689814d10b · outbound
This paper cites Federated learning for healthcare applications.IEEE Internet of Things Journal, 11(5):7339–7358, 2024.
Domain Adaptation Techniques for Natural and Medical Image Classification Federated learning for healthcare applications.IEEE Internet of Things Journal, 11(5):7339–7358, 2024
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:50:33.297714Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 5cd47494-5f43-4f5f-8057-75d0fab767d1 · outbound
This paper cites Adversarial domain adaptation with clip for few-shot image classification.
Domain Adaptation Techniques for Natural and Medical Image Classification Adversarial domain adaptation with clip for few-shot image classification
Reference 48
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:50:33.285167Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation fbe34382-4b69-43e0-842f-73f6e907ff4e · outbound
This paper cites Calibratingdeepneuralnetworksusing focal loss.Advances in Neural Information Processing Systems, 33:15288–15299, 2020.
Domain Adaptation Techniques for Natural and Medical Image Classification Calibratingdeepneuralnetworksusing focal loss.Advances in Neural Information Processing Systems, 33:15288–15299, 2020
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:50:33.272716Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 06fa506a-08b1-4da4-be2c-e8c5ce8ef876 · outbound
This paper cites Do CIFAR-10 Classifiers Generalize to CIFAR-10?.
Domain Adaptation Techniques for Natural and Medical Image Classification Do CIFAR-10 Classifiers Generalize to CIFAR-10?
Reference 50
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T16:50:33.198803Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
No inbound Pith citation observations are available.