Learning Private Representations through Entropy-based Adversarial Training | Paper Citation Record

Observation a11416cd-5ce2-4f8a-bff1-ec8ff6768df4 · outbound

This paper cites Deep learning with differential privacy.

Learning Private Representations through Entropy-based Adversarial Training Deep learning with differential privacy

Reference 1

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This paper cites Where- fore art thou r3579x?: anonymized social networks, hidden patterns, and structural steganography.

Learning Private Representations through Entropy-based Adversarial Training Where- fore art thou r3579x?: anonymized social networks, hidden patterns, and structural steganography

Reference 2

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Learning Private Representations through Entropy-based Adversarial Training Unresolved cited work

Reference 3

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Observation b0883275-130a-4831-9293-c1e453daa44a · outbound

This paper cites Federated disentangled representation learning for unsupervised brain anomaly detection.

Learning Private Representations through Entropy-based Adversarial Training Federated disentangled representation learning for unsupervised brain anomaly detection

Reference 4

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This paper cites Multi-level variational autoencoder: Learning disentangled representations from grouped observations.

Learning Private Representations through Entropy-based Adversarial Training Multi-level variational autoencoder: Learning disentangled representations from grouped observations

Reference 5

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Learning Private Representations through Entropy-based Adversarial Training Unresolved cited work

Reference 6

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Observation 2b950937-b89f-4bda-8ca9-3f9886007d2c · outbound

This paper cites Flexibly Fair Representation Learning by Disentanglement.

Learning Private Representations through Entropy-based Adversarial Training Flexibly Fair Representation Learning by Disentanglement

Reference 7

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Observation 1183f31c-c122-4318-9d47-b4b5e194eb7c · outbound

This paper cites Unsupervised learn- ing of disentangled representations from video.

Learning Private Representations through Entropy-based Adversarial Training Unsupervised learn- ing of disentangled representations from video

Reference 8

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Observation cf76f567-8934-4aa7-a561-0e0a6c42eb04 · outbound

This paper cites Guided variational autoencoder for disentanglement learning.

Learning Private Representations through Entropy-based Adversarial Training Guided variational autoencoder for disentanglement learning

Reference 9

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Observation d0aab52e-6a17-436b-97ae-4f3f1bb88273 · outbound

This paper cites Im- proving zero-shot learning by mitigating the hubness prob- lem, 2014.

Learning Private Representations through Entropy-based Adversarial Training Im- proving zero-shot learning by mitigating the hubness prob- lem, 2014

Reference 10

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Observation e2c1db39-6530-42df-841b-214ca7762ce3 · outbound

This paper cites Uci machine learning reposi- tory, 2017.

Learning Private Representations through Entropy-based Adversarial Training Uci machine learning reposi- tory, 2017

Reference 11

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Observation 4da579da-81b7-46de-8dc3-1e68c9741b3b · outbound

This paper cites Privacy-preserving image features via adversarial affine subspace embeddings.

Learning Private Representations through Entropy-based Adversarial Training Privacy-preserving image features via adversarial affine subspace embeddings

Reference 12

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Observation 5571b180-e665-4442-83f9-eb3ec9b72549 · outbound

This paper cites Differential privacy.

Learning Private Representations through Entropy-based Adversarial Training Differential privacy

Reference 13

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Observation 7ec32a53-2da5-4932-8ff7-03096c5ab7ea · outbound

This paper cites Exposed! a survey of attacks on private data.

Learning Private Representations through Entropy-based Adversarial Training Exposed! a survey of attacks on private data

Reference 14

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Observation 270da044-4564-4559-816f-92876aeccd91 · outbound

This paper cites Learning Anonymized Representations with Adversarial Neural Networks.

Learning Private Representations through Entropy-based Adversarial Training Learning Anonymized Representations with Adversarial Neural Networks

Reference 15

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Observation d4ca2c66-8bb9-47c8-81c0-316084d3c9ab · outbound

This paper cites Demystifying inter-class disentanglement.

Learning Private Representations through Entropy-based Adversarial Training Demystifying inter-class disentanglement

Reference 16

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Observation 3d61890e-a2fa-47e0-869c-84639bbc3f0c · outbound

This paper cites Unsupervised Domain Adaptation by Backpropagation.

Learning Private Representations through Entropy-based Adversarial Training Unsupervised Domain Adaptation by Backpropagation

Reference 17

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Observation ee14e768-f366-417b-9fde-256510f1806a · outbound

This paper cites Differentially Private Federated Learning: A Client Level Perspective.

Learning Private Representations through Entropy-based Adversarial Training Differentially Private Federated Learning: A Client Level Perspective

Reference 18

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This paper cites Jointly de- biasing face recognition and demographic attribute estima- tion.

Learning Private Representations through Entropy-based Adversarial Training Jointly de- biasing face recognition and demographic attribute estima- tion

Reference 19

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Observation 0bee20be-cb3a-4b2f-a39a-ccf129c9a6c4 · outbound

This paper cites Ms-celeb-1m: A dataset and benchmark for large-scale face recognition.

Learning Private Representations through Entropy-based Adversarial Training Ms-celeb-1m: A dataset and benchmark for large-scale face recognition

Reference 20

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Observation 6dd39ca5-7cdf-4cba-9664-926f0488f471 · outbound

This paper cites Minimax filter: Learning to preserve privacy from inference attacks.

Learning Private Representations through Entropy-based Adversarial Training Minimax filter: Learning to preserve privacy from inference attacks

Reference 21

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Observation 8db9cb7b-0d57-437d-88ec-c6f8da5da2ff · outbound

This paper cites Equality of op- portunity in supervised learning.

Learning Private Representations through Entropy-based Adversarial Training Equality of op- portunity in supervised learning

Reference 22

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Observation 52cfd11e-0436-481f-bd2a-3d3946afba8a · outbound

This paper cites Disentangling factors of variation with cycle- consistent variational auto-encoders.

Learning Private Representations through Entropy-based Adversarial Training Disentangling factors of variation with cycle- consistent variational auto-encoders

Reference 23

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Observation a1156aa2-6d25-4d01-9a47-c08531c3965b · outbound

This paper cites Deep Residual Learning for Image Recognition.

Learning Private Representations through Entropy-based Adversarial Training Deep Residual Learning for Image Recognition

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:47:05.548765Z

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Observation 156b05fd-9e54-4cfc-90b4-fa9c8d1b26da · outbound

This paper cites beta-V AE: Learning basic visual con- cepts with a constrained variational framework.

Learning Private Representations through Entropy-based Adversarial Training beta-V AE: Learning basic visual con- cepts with a constrained variational framework

Reference 25

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Observation cad214c7-6394-48a2-be43-996bb401bf35 · outbound

This paper cites Fades: Fair disentanglement with sensitive relevance.

Learning Private Representations through Entropy-based Adversarial Training Fades: Fair disentanglement with sensitive relevance

Reference 26

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Observation 00a97ad9-2724-46c8-9072-50ffb92f2385 · outbound

This paper cites Noisy adversarial representation learning for effective and efficient image obfuscation.

Learning Private Representations through Entropy-based Adversarial Training Noisy adversarial representation learning for effective and efficient image obfuscation

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:47:05.507415Z

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Observation 131b5ba8-2cfc-404c-8874-b202aada41ef · outbound

This paper cites Privacy-Net: An Adversarial Approach for Identity-Obfuscated Segmentation of Medical Images.

Learning Private Representations through Entropy-based Adversarial Training Privacy-Net: An Adversarial Approach for Identity-Obfuscated Segmentation of Medical Images

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:47:04.789435Z

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Observation 6deca337-ab96-4120-8e77-ec5cf9406891 · outbound

This paper cites Auto-Encoding Variational Bayes.

Learning Private Representations through Entropy-based Adversarial Training Auto-Encoding Variational Bayes

Reference 29

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Observation 446a163f-f40c-4606-a898-a495667bd6b5 · outbound

This paper cites Learning multiple layers of features from tiny images.

Learning Private Representations through Entropy-based Adversarial Training Learning multiple layers of features from tiny images

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:47:05.493800Z

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Observation 5818c5d7-f922-41e9-a1b3-dd2c86fc1ae1 · outbound

This paper cites Con- struction of an off-centered entropy for supervised learning.

Learning Private Representations through Entropy-based Adversarial Training Con- struction of an off-centered entropy for supervised learning

Reference 31

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Observation b6c23ee3-ff7f-453a-a37f-fbe5683dcccd · outbound

This paper cites DeepObfuscator: Obfuscating Intermediate Representations with Privacy-Preserving Adversarial Learning on Smartphones.

Learning Private Representations through Entropy-based Adversarial Training DeepObfuscator: Obfuscating Intermediate Representations with Privacy-Preserving Adversarial Learning on Smartphones

Reference 32

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Observation 5143366e-bd09-4b14-8fff-5744f7cf2b0e · outbound

This paper cites Fair representation learning: An alternative to mutual information.

Learning Private Representations through Entropy-based Adversarial Training Fair representation learning: An alternative to mutual information

Reference 33

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Observation 2bbb8076-2adb-414f-ab60-f4efff56aef2 · outbound

This paper cites Fair transfer learning with factor variational auto-encoder.

Learning Private Representations through Entropy-based Adversarial Training Fair transfer learning with factor variational auto-encoder

Reference 34

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Observation 781f1db7-8656-4a66-bfab-42c4e19c3dc1 · outbound

This paper cites Exploring disentangled feature rep- resentation beyond face identification.

Learning Private Representations through Entropy-based Adversarial Training Exploring disentangled feature rep- resentation beyond face identification

Reference 35

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Observation 4d796bf8-1a94-4b24-925a-ac1e4f28645a · outbound

This paper cites On the fairness of disentangled representations.

Learning Private Representations through Entropy-based Adversarial Training On the fairness of disentangled representations

Reference 36

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Observation 736b6cad-1be0-48ce-8bc9-713fd19832be · outbound

This paper cites The Variational Fair Autoencoder.

Learning Private Representations through Entropy-based Adversarial Training The Variational Fair Autoencoder

Reference 37

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This paper cites The variational fair autoencoder, 2017.

Learning Private Representations through Entropy-based Adversarial Training The variational fair autoencoder, 2017

Reference 38

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Observation 89066fee-ef82-4de0-9b74-1112d212a457 · outbound

This paper cites Learning Adversarially Fair and Transferable Representations.

Learning Private Representations through Entropy-based Adversarial Training Learning Adversarially Fair and Transferable Representations

Reference 39

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Observation f106be87-9049-4259-9c21-a5bcee2aaef1 · outbound

This paper cites Communication-Efficient Learning of Deep Networks from Decentralized Data.

Learning Private Representations through Entropy-based Adversarial Training Communication-Efficient Learning of Deep Networks from Decentralized Data

Reference 40

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Observation 090127bf-42a8-4397-8695-161dfe699337 · outbound

This paper cites Privacy–enhancing face biometrics: A com- prehensive survey.IEEE Transactions on Information Foren- sics and Security, 16:4147–4183, 2021.

Learning Private Representations through Entropy-based Adversarial Training Privacy–enhancing face biometrics: A com- prehensive survey.IEEE Transactions on Information Foren- sics and Security, 16:4147–4183, 2021

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:47:05.380192Z

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Observation cb314db6-31f7-4070-ac5a-584bb9e0a3a7 · outbound

This paper cites How To Break Anonymity of the Netflix Prize Dataset.

Learning Private Representations through Entropy-based Adversarial Training How To Break Anonymity of the Netflix Prize Dataset

Reference 42

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Observation 89d2fbf0-b466-4cd2-861c-fafb15c50900 · outbound

This paper cites Panda: Unsupervised learning of parts and appearances in the feature maps of GANs.

Learning Private Representations through Entropy-based Adversarial Training Panda: Unsupervised learning of parts and appearances in the feature maps of GANs

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:47:05.364522Z

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Observation 8d0667c0-32cd-4509-a5e5-6e85f2543d90 · outbound

This paper cites Privacy-Preserving Deep Inference for Rich User Data on The Cloud.

Learning Private Representations through Entropy-based Adversarial Training Privacy-Preserving Deep Inference for Rich User Data on The Cloud

Reference 44

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:47:04.681695Z

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Observation 294e5230-6667-4bac-b3b9-a426c3a44e99 · outbound

This paper cites Deep private-feature extraction.

Learning Private Representations through Entropy-based Adversarial Training Deep private-feature extraction

Reference 45

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Observation 53979623-d96a-4573-9824-17686907b57a · outbound

This paper cites Learning privacy preserving encodings through adversarial training.

Learning Private Representations through Entropy-based Adversarial Training Learning privacy preserving encodings through adversarial training

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:47:05.334275Z

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Observation 90116fbc-524c-41a4-af4f-e566d701e256 · outbound

This paper cites Discovering fair representations in the data domain.

Learning Private Representations through Entropy-based Adversarial Training Discovering fair representations in the data domain

Reference 47

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Observation 895f53de-2baa-4be6-98bf-f7d05c636e12 · outbound

This paper cites Hubs in space: Popular nearest neighbors in high- dimensional data.

Learning Private Representations through Entropy-based Adversarial Training Hubs in space: Popular nearest neighbors in high- dimensional data

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:47:05.304221Z

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Observation 27553d1b-2e28-4064-9d24-18f7e40dc594 · outbound

This paper cites Mitigat- ing information leakage in image representations: A maxi- mum entropy approach.

Learning Private Representations through Entropy-based Adversarial Training Mitigat- ing information leakage in image representations: A maxi- mum entropy approach

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:47:05.289133Z

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Observation 0c6f5395-06d8-4a5e-a200-3b4486a77a2d · outbound

This paper cites Privacy-preserving human activity recog- nition from extreme low resolution.

Learning Private Representations through Entropy-based Adversarial Training Privacy-preserving human activity recog- nition from extreme low resolution

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:47:05.274117Z

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Observation b73ab8da-7bc1-4776-bf7f-1a8e8831240a · outbound

This paper cites On the global optima of kernelized adversarial representation learn- ing.

Learning Private Representations through Entropy-based Adversarial Training On the global optima of kernelized adversarial representation learn- ing

Reference 51

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Observation f050dcf5-a3f8-4877-837f-5ea5ae01a3e6 · outbound

This paper cites On the global optima of kernelized adversarial representation learn- ing.

Learning Private Representations through Entropy-based Adversarial Training On the global optima of kernelized adversarial representation learn- ing

Reference 52

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Observation 1fa8ef49-3ad0-4d80-96bd-e40d1baec8fd · outbound

This paper cites Updates-Leak: Data Set Inference and Reconstruction Attacks in Online Learning.

Learning Private Representations through Entropy-based Adversarial Training Updates-Leak: Data Set Inference and Reconstruction Attacks in Online Learning

Reference 53

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:47:04.662004Z

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Observation ccbf0ff3-3c3e-452f-8537-1c550dc5ad87 · outbound

This paper cites Improved techniques for training gans.

Learning Private Representations through Entropy-based Adversarial Training Improved techniques for training gans

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:47:05.230432Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c24ca5fc-af9d-4c07-93ce-ea0a5ef75736 · outbound

This paper cites On the fairness of privacy-preserving rep- resentations in medical applications.

Learning Private Representations through Entropy-based Adversarial Training On the fairness of privacy-preserving rep- resentations in medical applications

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:47:05.215525Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 188ab1de-3d00-41de-bddc-3c628dc25c00 · outbound

This paper cites Share your representation only: Guar- anteed improvement of the privacy-utility tradeoff in feder- ated learning.

Learning Private Representations through Entropy-based Adversarial Training Share your representation only: Guar- anteed improvement of the privacy-utility tradeoff in feder- ated learning

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:47:05.201550Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 43b25406-f410-40d9-811c-0e30720e7c3c · outbound

This paper cites Privacy-preserving deep learning.

Learning Private Representations through Entropy-based Adversarial Training Privacy-preserving deep learning

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:47:05.185665Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a648cbca-5b52-4be9-9064-ca4838ad689c · outbound

This paper cites Privacy-preserving adversarial rep- resentation learning in asr: Reality or illusion? Proc.

Learning Private Representations through Entropy-based Adversarial Training Privacy-preserving adversarial rep- resentation learning in asr: Reality or illusion? Proc

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:47:05.171369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 90587dd3-d59b-4b29-9b7f-46bc2278fbaa · outbound

This paper cites Challenges in disentangling in- dependent factors of variation, 2018.

Learning Private Representations through Entropy-based Adversarial Training Challenges in disentangling in- dependent factors of variation, 2018

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:47:05.157055Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 3c15fc8b-94a7-4eca-a274-2ca27ab14c23 · outbound

This paper cites Fairness-aware adver- sarial perturbation towards bias mitigation for deployed deep models.

Learning Private Representations through Entropy-based Adversarial Training Fairness-aware adver- sarial perturbation towards bias mitigation for deployed deep models

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:47:05.143404Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6af7a5ea-0650-4615-a0b4-8d5858194486 · outbound

This paper cites Privacy-preserving deep action recogni- tion: An adversarial learning framework and a new dataset.

Learning Private Representations through Entropy-based Adversarial Training Privacy-preserving deep action recogni- tion: An adversarial learning framework and a new dataset

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:47:05.129538Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 48f98a0a-3abd-4649-a976-4328af6af1f1 · outbound

This paper cites Adversarial learning of privacy-preserving and task-oriented representations.

Learning Private Representations through Entropy-based Adversarial Training Adversarial learning of privacy-preserving and task-oriented representations

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:47:05.116671Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e0b84577-ea01-4653-b764-f0962428d57d · outbound

This paper cites Controllable invariance through adversarial feature learning.

Learning Private Representations through Entropy-based Adversarial Training Controllable invariance through adversarial feature learning

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:47:05.103135Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 357089f8-879f-4fe4-b361-d1a64a80a1c6 · outbound

This paper cites Investigating bias and fairness in facial expression recogni- tion.

Learning Private Representations through Entropy-based Adversarial Training Investigating bias and fairness in facial expression recogni- tion

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:47:05.090400Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 3913f73b-4c05-473e-8c39-1cd43674a9f3 · outbound

This paper cites Enhancing privacy in face analytics using fully homomorphic encryption.

Learning Private Representations through Entropy-based Adversarial Training Enhancing privacy in face analytics using fully homomorphic encryption

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:47:05.076296Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 17898d47-299c-4da0-ad74-3f6d2677ef2f · outbound

This paper cites The extended yale face database b.

Learning Private Representations through Entropy-based Adversarial Training The extended yale face database b

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:47:05.061956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e1318814-1fdc-4eba-8706-68e252fd13d4 · outbound

This paper cites Learning informative and private representations via generative adversarial networks.

Learning Private Representations through Entropy-based Adversarial Training Learning informative and private representations via generative adversarial networks

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:47:05.048581Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e82f0dde-a5c3-442e-8709-18d59f2b6280 · outbound

This paper cites Kitani, and Yoichi Sato.

Learning Private Representations through Entropy-based Adversarial Training Kitani, and Yoichi Sato

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:47:05.035325Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6f206be9-b92b-48ea-a401-9ba7394973e6 · outbound

This paper cites Boosting demographic fairness of face attribute classifiers via latent adversarial representations.

Learning Private Representations through Entropy-based Adversarial Training Boosting demographic fairness of face attribute classifiers via latent adversarial representations

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:47:05.021930Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ef610ec8-a175-4088-9f57-60688ed7e2a0 · outbound

This paper cites # !!"# !$%! Residual Target Enc Dec.

Learning Private Representations through Entropy-based Adversarial Training # !!"# !$%! Residual Target Enc Dec

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:47:05.007880Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7305fd94-b273-46cc-b4e1-db01b48845be · outbound

This paper cites shortcuts.

Learning Private Representations through Entropy-based Adversarial Training shortcuts

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:47:04.993179Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 3b3e204a-0161-4f31-a18b-abcec3e2faf3 · outbound

This paper cites an unresolved cited work.

Learning Private Representations through Entropy-based Adversarial Training Unresolved cited work

Reference 73

Resolution: unresolved; raw_fallback, observed 2026-08-06T17:47:04.977712Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 224e9ad5-a25c-4c9e-b444-8ba234057c32 · outbound

This paper cites We thereby largely follow the pro- tocol of [23, 52].

Learning Private Representations through Entropy-based Adversarial Training We thereby largely follow the pro- tocol of [23, 52]

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:47:04.964176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d625a56f-e621-4850-b7a4-a3e645bd4c91 · outbound

This paper cites one-to-one.

Learning Private Representations through Entropy-based Adversarial Training one-to-one

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:47:04.950633Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation fd0add0c-ec0f-49e2-802a-ab5a611d902d · outbound

This paper cites an unresolved cited work.

Learning Private Representations through Entropy-based Adversarial Training Unresolved cited work

Reference 76

Resolution: unresolved; raw_fallback, observed 2026-08-06T17:47:04.936881Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 67039767-e774-47e8-a389-9ab6140df8e5 · outbound

This paper cites This visu- alization provides a more in-depth view of how the adver- sarial process leads to a remapping of identities.

Learning Private Representations through Entropy-based Adversarial Training This visu- alization provides a more in-depth view of how the adver- sarial process leads to a remapping of identities

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:47:04.921578Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f987cac8-3827-4a48-9fd8-f8b4c96f81d3 · outbound

This paper cites relatively unbiased.

Learning Private Representations through Entropy-based Adversarial Training relatively unbiased

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:47:04.906979Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 22dff14e-a876-474c-9025-06e8dfe78fcf · outbound

This paper cites an unresolved cited work.

Learning Private Representations through Entropy-based Adversarial Training Unresolved cited work

Reference 79

Resolution: unresolved; raw_fallback, observed 2026-08-06T17:47:04.893284Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c8777445-f751-4ee5-8260-a0eef8e1b05f · outbound

This paper cites Each column is two different samples from CelebA (one male and one female), and from top to bot- tom, the privacy disclosure is decreasing for each.

Learning Private Representations through Entropy-based Adversarial Training Each column is two different samples from CelebA (one male and one female), and from top to bot- tom, the privacy disclosure is decreasing for each

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:47:04.878851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation dc95a4cf-3b01-4233-a650-96ee421c7436 · outbound

This paper cites Ta- ble 10 shows the architectures of the V AE, i.e., the encoder and the decoder.

Learning Private Representations through Entropy-based Adversarial Training Ta- ble 10 shows the architectures of the V AE, i.e., the encoder and the decoder

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:47:04.865561Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b6548975-6c1b-41ab-9e57-75868bf11da0 · outbound

This paper cites an unresolved cited work.

Learning Private Representations through Entropy-based Adversarial Training Unresolved cited work

Reference 2022

Resolution: unresolved; raw_fallback, observed 2026-08-06T17:47:05.450660Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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