Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data | Paper Citation Record

Observation 7066c29b-c79f-4a9e-8d1b-9fbf941ca8a8 · outbound

This paper cites Denoising diffusion probabilistic models.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Denoising diffusion probabilistic models

Reference 1

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Observation 160e7fc3-c4b8-48d6-babb-eaec4768122e · outbound

This paper cites Generative adversarial nets.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Generative adversarial nets

Reference 2

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Observation 5a13c328-71bf-4714-a946-26d1c6e2c47f · outbound

This paper cites Generating diverse high- fidelity images with vq-vae-2.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Generating diverse high- fidelity images with vq-vae-2

Reference 3

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Observation a09a7b70-fabe-48d6-a7c5-d4298c07bc52 · outbound

This paper cites Conditional Generative Adversarial Nets.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Conditional Generative Adversarial Nets

Reference 4

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Observation 2128ad64-d331-4c36-9a04-7aa3057a4c9c · outbound

This paper cites Ganwriting: Content-conditioned generation of styled handwritten word images.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Ganwriting: Content-conditioned generation of styled handwritten word images

Reference 5

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Observation b128c367-de47-4f2d-bdf4-93531d4e1c06 · outbound

This paper cites Csigan: Robust channel state information-based activity recognition with gans.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Csigan: Robust channel state information-based activity recognition with gans

Reference 6

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Observation 26b0a437-93f3-42a7-b047-f37c0917feee · outbound

This paper cites Cross-frequency training with adversarial learning for radar micro-doppler signature classification.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Cross-frequency training with adversarial learning for radar micro-doppler signature classification

Reference 7

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Observation e2e16c49-d1c7-4e36-9f9e-b448370900cb · outbound

This paper cites Fido: Ubiquitous fine-grained wifi-based localization for unlabelled users via domain adaptation.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Fido: Ubiquitous fine-grained wifi-based localization for unlabelled users via domain adaptation

Reference 8

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Observation fefad10c-bef9-4edc-a77e-2f790a1eeecf · outbound

This paper cites Rf-diffusion: Radio signal generation via time-frequency diffusion.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Rf-diffusion: Radio signal generation via time-frequency diffusion

Reference 9

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Observation 5c82f67f-74e0-4de4-9ccd-09448ab15418 · outbound

This paper cites Rf genesis: Zero-shot generalization of mmwave sensing through simulation-based data synthesis and generative diffusion models.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Rf genesis: Zero-shot generalization of mmwave sensing through simulation-based data synthesis and generative diffusion models

Reference 10

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Observation f944cc80-ef9c-4720-b041-7d7f3ac5ebc4 · outbound

This paper cites Crossgr: Accurate and low-cost cross-target gesture recognition using wi-fi.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Crossgr: Accurate and low-cost cross-target gesture recognition using wi-fi

Reference 11

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Observation ad73fefd-bb44-4084-9253-739559ef3cdd · outbound

This paper cites Fidora: Robust wifi-based indoor localization via unsupervised domain adaptation.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Fidora: Robust wifi-based indoor localization via unsupervised domain adaptation

Reference 12

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Observation 456818d7-06e6-4374-abb7-005b21600376 · outbound

This paper cites Medical image generation using generative adversarial networks: A review.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Medical image generation using generative adversarial networks: A review

Reference 13

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Observation e6edeb3f-bc35-40ec-97e7-74cd8cb082dc · outbound

This paper cites Cross-domain wifi sensing with channel state information: A survey.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Cross-domain wifi sensing with channel state information: A survey

Reference 14

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Observation b8e0c48c-573b-46fc-8234-ae420f800602 · outbound

This paper cites Learning to sense: Deep learning for wireless sensing with less training efforts.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Learning to sense: Deep learning for wireless sensing with less training efforts

Reference 15

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Observation 6e6de35f-b299-48c0-8400-13a1d276e5ee · outbound

This paper cites What does dall-e 2 know about radiology? Journal of Medical Internet Research, 2023.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data What does dall-e 2 know about radiology? Journal of Medical Internet Research, 2023

Reference 16

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Observation 0714610d-567d-4e24-9942-64df32089db6 · outbound

This paper cites Adapting Pretrained Vision-Language Foundational Models to Medical Imaging Domains.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Adapting Pretrained Vision-Language Foundational Models to Medical Imaging Domains

Reference 17

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Observation 5509b062-21d4-46d4-885b-6b2d5905b55e · outbound

This paper cites Aligning synthetic medical images with clinical knowledge using human feedback.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Aligning synthetic medical images with clinical knowledge using human feedback

Reference 18

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Observation ff1fa160-0337-4a54-8aba-f2a841750420 · outbound

This paper cites How faithful is your synthetic data? sample-level metrics for evaluating and auditing generative models.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data How faithful is your synthetic data? sample-level metrics for evaluating and auditing generative models

Reference 19

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Observation 168c03c6-0ed1-4c6f-b3a7-62fee3d85fc9 · outbound

This paper cites Synthetic data in machine learning for medicine and healthcare.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Synthetic data in machine learning for medicine and healthcare

Reference 20

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Observation aa648f9f-0c0d-43e5-aee0-610484de79bd · outbound

This paper cites Are gans created equal? a large-scale study.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Are gans created equal? a large-scale study

Reference 21

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Observation 64a91bdc-8518-4e52-ab5e-8488e75602f4 · outbound

This paper cites Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models

Reference 22

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Observation 443b42f9-3e3d-4670-b650-c3aa79429798 · outbound

This paper cites Uwb-fi: Pushing wi-fi towards ultra-wideband for fine-granularity sensing.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Uwb-fi: Pushing wi-fi towards ultra-wideband for fine-granularity sensing

Reference 23

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Observation 8d31b892-579f-4212-9e25-78b12f8ce331 · outbound

This paper cites MMBind: Unleashing the Potential of Distributed and Heterogeneous Data for Multimodal Learning in IoT.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data MMBind: Unleashing the Potential of Distributed and Heterogeneous Data for Multimodal Learning in IoT

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-08-06T21:54:48.872027Z

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Observation d92a55f6-751f-44d9-8b7b-4459af2bb834 · outbound

This paper cites LLMSense: Harnessing LLMs for High-level Reasoning Over Spatiotemporal Sensor Traces.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data LLMSense: Harnessing LLMs for High-level Reasoning Over Spatiotemporal Sensor Traces

Reference 25

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Observation dd546146-510e-407f-ac36-aa0f305a1a8e · outbound

This paper cites Babel: A Scalable Pre-trained Model for Multi-Modal Sensing via Expandable Modality Alignment.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Babel: A Scalable Pre-trained Model for Multi-Modal Sensing via Expandable Modality Alignment

Reference 26

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Observation 545da985-d5a2-44fe-a3f6-c633f8add23a · outbound

This paper cites How good is my gan? In Springer ECCV, 2018.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data How good is my gan? In Springer ECCV, 2018

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:54:49.868884Z

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Observation efe516b3-51e0-48b1-95dd-7f6543abf222 · outbound

This paper cites Spectrally-normalized margin bounds for neural networks.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Spectrally-normalized margin bounds for neural networks

Reference 28

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Observation 3b590b94-82d9-404c-94cc-b4314a42e97d · outbound

This paper cites Large margin deep networks for classification.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Large margin deep networks for classification

Reference 29

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Observation 2d7f2529-0921-438a-9b73-d6df5b29504e · outbound

This paper cites Identifying mislabeled data using the area under the margin ranking.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Identifying mislabeled data using the area under the margin ranking

Reference 30

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Observation 9f3de8d8-a5cc-439c-b090-61904d59f00c · outbound

This paper cites Understanding deep learning (still) requires rethinking generalization.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Understanding deep learning (still) requires rethinking generalization

Reference 31

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Observation 2f440146-270c-41c1-9b14-15f2081d8a2f · outbound

This paper cites Towards generalized mmwave-based human pose estimation through signal augmentation.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Towards generalized mmwave-based human pose estimation through signal augmentation

Reference 32

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Observation 0bfcb75f-c7ed-4185-9931-11bc758a02fc · outbound

This paper cites Quality aware generative adversarial networks.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Quality aware generative adversarial networks

Reference 33

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Observation 2fbd319f-e495-4c0a-a718-a97fdff8cdd3 · outbound

This paper cites Classification accuracy score for conditional generative models.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Classification accuracy score for conditional generative models

Reference 34

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Observation 4feed5fb-2487-4861-b521-a0b41c9cd2cd · outbound

This paper cites Teaching rf to sense without rf training measurements.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Teaching rf to sense without rf training measurements

Reference 35

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Observation 8162a35c-f3f2-49c1-8ce1-5733b063f9f9 · outbound

This paper cites Wifi sensing with channel state information: A survey.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Wifi sensing with channel state information: A survey

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:54:49.730574Z

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Observation 1d899e13-6960-4f83-892f-9ac6b48f9c02 · outbound

This paper cites Survey of time series data generation in iot.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Survey of time series data generation in iot

Reference 37

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Observation 640bb2ac-26fc-47c8-8028-39f455968699 · outbound

This paper cites Rfboost: Understanding and boosting deep wifi sensing via physical data augmentation.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Rfboost: Understanding and boosting deep wifi sensing via physical data augmentation

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:54:49.698832Z

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This paper cites Simple and effective augmentation methods for csi based indoor localization.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Simple and effective augmentation methods for csi based indoor localization

Reference 39

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This paper cites Data augmentation techniques for cross- domain wifi csi-based human activity recognition.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Data augmentation techniques for cross- domain wifi csi-based human activity recognition

Reference 40

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Observation 3802b105-4f7d-4ba9-a63d-6c7be057ff9c · outbound

This paper cites Ray tracing as a design tool for radio networks.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Ray tracing as a design tool for radio networks

Reference 41

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Observation 838b7924-a497-4895-9865-32930c768e6c · outbound

This paper cites Nerf2: Neural radio- frequency radiance fields.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Nerf2: Neural radio- frequency radiance fields

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:54:49.630484Z

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Observation 0f8508d1-ce3c-4df6-80e0-65c42b1ae238 · outbound

This paper cites Food and liquid sensing in practical environments using{RFIDs}.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Food and liquid sensing in practical environments using{RFIDs}

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:54:49.614607Z

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Observation e9f435bd-ec6d-4e61-8b24-d8e65f7ac39d · outbound

This paper cites Survey on synthetic data generation, evaluation methods and gans.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Survey on synthetic data generation, evaluation methods and gans

Reference 44

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Observation a96bf69e-b60c-40ce-a5e7-d086068cabe8 · outbound

This paper cites Pros and cons of gan evaluation measures.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Pros and cons of gan evaluation measures

Reference 45

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Observation 546552da-b18b-461a-8320-69b79133492c · outbound

This paper cites Quality aware generative adversarial networks.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Quality aware generative adversarial networks

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:54:49.566602Z

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Observation 2c7c9df7-e041-4e84-a0a2-0c666b7de9a5 · outbound

This paper cites Gans trained by a two time-scale update rule converge to a local nash equilibrium.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Gans trained by a two time-scale update rule converge to a local nash equilibrium

Reference 47

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Observation a2234acc-9d39-4c76-b74c-f8a7f9339025 · outbound

This paper cites Categorical generative model evaluation via synthetic distribution coarsening.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Categorical generative model evaluation via synthetic distribution coarsening

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:54:49.540225Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 9d9e8627-2927-4bba-897b-cad002bfb495 · outbound

This paper cites Bayes’ theorem — Wikipedia, the free encyclopedia,.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Bayes’ theorem — Wikipedia, the free encyclopedia,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:54:49.523794Z

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Observation 59c3be33-d7f2-4d08-a81c-2d1c0243d45d · outbound

This paper cites Medical image synthesis with context-aware generative adversarial networks.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Medical image synthesis with context-aware generative adversarial networks

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:54:49.493116Z

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Observation 75520472-a56b-4e23-8cc2-10113f06ed72 · outbound

This paper cites Cross-scenario device-free activity recognition based on deep adversarial networks.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Cross-scenario device-free activity recognition based on deep adversarial networks

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:54:49.478084Z

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Observation 73cfa16f-c206-4382-8773-173e08fb81b2 · outbound

This paper cites A deep-learning-based self-calibration time-reversal fingerprinting localization approach on wi-fi plat- form.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data A deep-learning-based self-calibration time-reversal fingerprinting localization approach on wi-fi plat- form

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:54:49.452802Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 22e728ef-2d3d-42d8-b1da-c771973f044a · outbound

This paper cites Taming the inconsistency of wi-fi fingerprints for device-free passive indoor localization.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Taming the inconsistency of wi-fi fingerprints for device-free passive indoor localization

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:54:49.436423Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation aa716fe9-fed5-4c01-a82d-1a1d3f02a8f9 · outbound

This paper cites Unsupervised and semi-supervised learning with categorical generative adversarial networks.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Unsupervised and semi-supervised learning with categorical generative adversarial networks

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:54:49.419193Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 9df05463-80b2-4bd9-9000-cce7405315f7 · outbound

This paper cites Af-dcgan: Amplitude feature deep convolutional gan for fingerprint construc- tion in indoor localization systems.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Af-dcgan: Amplitude feature deep convolutional gan for fingerprint construc- tion in indoor localization systems

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:54:49.402604Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 2e49d10d-d825-48f6-940e-297212b84828 · outbound

This paper cites Deep residual learning for image recognition.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Deep residual learning for image recognition

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:54:49.385222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 43b63e12-1c80-4d00-8e37-b5239b4eee64 · outbound

This paper cites Sensefi: A library and benchmark on deep-learning- empowered wifi human sensing.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Sensefi: A library and benchmark on deep-learning- empowered wifi human sensing

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:54:49.352701Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 700a2304-5924-4019-8baf-926c4513d9a2 · outbound

This paper cites Signfi: Sign language recognition using wifi.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Signfi: Sign language recognition using wifi

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:54:49.325306Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 7aada655-3391-478a-9151-613b378ce105 · outbound

This paper cites Zero-effort cross-domain gesture recognition with wi-fi.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Zero-effort cross-domain gesture recognition with wi-fi

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:54:49.308888Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 0a11fe43-1f68-4308-88c4-14164003052c · outbound

This paper cites On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-06T21:54:48.529530Z

Source-reported events for the cited work

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Observation 1f29a794-0d57-4e9d-b87f-62c54a5c1c76 · outbound

This paper cites Sensitivity and Generalization in Neural Networks: an Empirical Study.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Sensitivity and Generalization in Neural Networks: an Empirical Study

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-06T21:54:48.536949Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8ca39401-3e02-44f9-b41f-b1ec36e99ee5 · outbound

This paper cites The jensen-shannon divergence.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data The jensen-shannon divergence

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:54:49.292233Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 54c74f16-5c49-4608-a2f2-4f4be1fd8c33 · outbound

This paper cites Position and orientation agnostic gesture recognition using wifi.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Position and orientation agnostic gesture recognition using wifi

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:54:49.277792Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 6ecc8126-0143-46a5-9aa1-cd0c52e42181 · outbound

This paper cites Diffar: Adaptive condi- tional diffusion model for temporal-augmented human activity recognition.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Diffar: Adaptive condi- tional diffusion model for temporal-augmented human activity recognition

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:54:49.261098Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 84edd70b-7657-41eb-a68e-af9d62b76b82 · outbound

This paper cites Opencos: Con- trastive semi-supervised learning for handling open-set unlabeled data.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Opencos: Con- trastive semi-supervised learning for handling open-set unlabeled data

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:54:49.243803Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 0c1593df-4e5d-4e7d-9e04-f1c542f6ffcb · outbound

This paper cites Iomatch: Simplifying open-set semi-supervised learning with joint inliers and outliers utilization.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Iomatch: Simplifying open-set semi-supervised learning with joint inliers and outliers utilization

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:54:49.227643Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 982addaa-bad3-4c34-a9b1-42032dd266a5 · outbound

This paper cites Ovanet: One-vs-all network for universal domain adaptation.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Ovanet: One-vs-all network for universal domain adaptation

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:54:49.209542Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 281894df-b8f9-4822-997a-0932afe5c36a · outbound

This paper cites Temporal Ensembling for Semi-Supervised Learning.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Temporal Ensembling for Semi-Supervised Learning

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-06T21:54:48.570888Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ad5a1318-9226-4b04-9fbc-8245be8e087b · outbound

This paper cites Proakis and M.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Proakis and M

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:54:49.192935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 47cbdb49-c809-4db2-af41-1dc2c23f4702 · outbound

This paper cites Fixmatch: Simplifying semi-supervised learning with consistency and confidence.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Fixmatch: Simplifying semi-supervised learning with consistency and confidence

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:54:49.178256Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f0ba9e0b-fc76-43b1-a155-33d6b540731f · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Pytorch: An imperative style, high-performance deep learning library

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:54:49.162318Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 61b482f2-bc24-423f-9bbc-2cebc0d80e64 · outbound

This paper cites Csigan: Robust channel state information-based activity recognition with gans.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Csigan: Robust channel state information-based activity recognition with gans

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:54:49.147432Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 61001edb-89cc-4ad7-bff2-4a209c34b740 · outbound

This paper cites Rfboost: Understanding and boosting deep wifi sensing via physical data augmentation.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Rfboost: Understanding and boosting deep wifi sensing via physical data augmentation

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:54:49.130934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c144b05c-ec0b-4680-a9e3-d92d0407cea1 · outbound

This paper cites Conditional Generation from Unconditional Diffusion Models using Denoiser Representations.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Conditional Generation from Unconditional Diffusion Models using Denoiser Representations

Reference 74

Resolution: verified exact; local_arxiv, observed 2026-08-06T21:54:48.769759Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation aafd45dc-c27a-4905-8e7c-0f9fa7b2cd7f · outbound

This paper cites Vaes meet diffusion models: Efficient and high-fidelity generation.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Vaes meet diffusion models: Efficient and high-fidelity generation

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:54:49.115773Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ce3449db-cf4f-412d-86ba-d1136cb9ef57 · outbound

This paper cites Affinity and Diversity: Quantifying Mechanisms of Data Augmentation.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Affinity and Diversity: Quantifying Mechanisms of Data Augmentation

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-06T21:54:48.616182Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3915f170-fbb1-4837-851c-553b7953b24a · outbound

This paper cites Hide-and-seek privacy challenge: Synthetic data generation vs.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Hide-and-seek privacy challenge: Synthetic data generation vs

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:54:49.100601Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c7af27d5-85c4-44ef-9286-80471c188ad6 · outbound

This paper cites Flow-gan: Combining maximum likelihood and adversarial learning in generative models.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Flow-gan: Combining maximum likelihood and adversarial learning in generative models

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:54:49.083837Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 69855208-c866-4d50-9064-7aad034750b2 · outbound

This paper cites A complete recipe for diffusion generative models.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data A complete recipe for diffusion generative models

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:54:49.064916Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 2441be01-8b64-4690-a9c8-06c5f890b9f7 · outbound

This paper cites Newrf: A deep learning framework for wireless radiation field reconstruction and channel prediction.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Newrf: A deep learning framework for wireless radiation field reconstruction and channel prediction

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:54:49.045827Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 9329eb9f-80ba-40b3-a658-c9f6f951867b · outbound

This paper cites A review on outlier/anomaly detection in time series data.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data A review on outlier/anomaly detection in time series data

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:54:49.028585Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b20db86b-a42f-4ccf-9806-9d8a38d4bd49 · outbound

This paper cites Deep learning for anomaly detection.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Deep learning for anomaly detection

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:54:49.011327Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f67f1948-ab77-4751-ab33-a06b9b31ce8c · outbound

This paper cites Model collapse demystified: The case of regression.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Model collapse demystified: The case of regression

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:54:48.991486Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 94c736bb-5c5b-47c0-8394-97b8b26ab66e · outbound

This paper cites A tale of tails: Model collapse as a change of scaling laws.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data A tale of tails: Model collapse as a change of scaling laws

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:54:48.972601Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c2949b1b-d18b-4220-a40b-284cb633e138 · outbound

This paper cites Beyond model collapse: Scaling up with synthesized data requires verification.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Beyond model collapse: Scaling up with synthesized data requires verification

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:54:48.953393Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 6f2f93b0-8654-4d8b-aa8e-03ff675187f5 · outbound

This paper cites Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:54:48.936661Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 2bdfb1d3-f78b-4590-ac5a-11cac2beb651 · outbound

This paper cites Total variation distance of probability measures — Wikipedia, the free encyclopedia, 2025.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Total variation distance of probability measures — Wikipedia, the free encyclopedia, 2025

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:54:48.921657Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f4551980-92d0-4767-8a01-79691bc05a06 · outbound

This paper cites an unresolved cited work.

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data Unresolved cited work

Reference 2025

Resolution: unresolved; raw_fallback, observed 2026-08-06T21:54:49.507530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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