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Paper Citation Record · LEDGER

Disentanglement in Difference: Directly Learning Semantically Disentangled Representations by Maximizing Inter-Factor Differences

As of 21 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2502.03123.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2502.03123 v2

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T05:54:34.190336Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

36 of 36 outbound references displayed

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  • verified fuzzy13
  • unresolved21
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation abf1b72f-ca70-4ff1-a41a-aeed5c9242af · outbound

This paper cites Wasserstein generative adversarial networks.

Disentanglement in Difference: Directly Learning Semantically Disentangled Representations by Maximizing Inter-Factor Differences Wasserstein generative adversarial networks

Reference 1

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Observation f348bd2a-9224-47ae-8348-84a4849621c8 · outbound

This paper cites Representation learning: A review and new perspectives.

Disentanglement in Difference: Directly Learning Semantically Disentangled Representations by Maximizing Inter-Factor Differences Representation learning: A review and new perspectives

Reference 2

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Observation c41cf7eb-3a40-424b-8402-c2c13cb6036f · outbound

This paper cites Scaling learning algorithms toward ai.

Disentanglement in Difference: Directly Learning Semantically Disentangled Representations by Maximizing Inter-Factor Differences Scaling learning algorithms toward ai

Reference 3

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Observation dc8ef21e-83a4-4244-a65d-76e7efcf44c6 · outbound

This paper cites and Kim, H.

Disentanglement in Difference: Directly Learning Semantically Disentangled Representations by Maximizing Inter-Factor Differences and Kim, H

Reference 4

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Observation 1d661ca0-1f40-41bf-8534-9c0ec802513f · outbound

This paper cites Understanding disentangling in $\beta$-VAE.

Disentanglement in Difference: Directly Learning Semantically Disentangled Representations by Maximizing Inter-Factor Differences Understanding disentangling in $\beta$-VAE

Reference 5

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Observation 761ded26-49dc-4c72-91da-f3760fb6794b · outbound

This paper cites Measuring disentanglement: A review of metrics.

Disentanglement in Difference: Directly Learning Semantically Disentangled Representations by Maximizing Inter-Factor Differences Measuring disentanglement: A review of metrics

Reference 6

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Observation b0dce0f0-e56f-44b7-92d3-b3f3c5f3ccba · outbound

This paper cites T., Li, X., Grosse, R.

Disentanglement in Difference: Directly Learning Semantically Disentangled Representations by Maximizing Inter-Factor Differences T., Li, X., Grosse, R

Reference 7

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Observation da8e83a6-665d-43dc-934a-c7526aa1bd74 · outbound

This paper cites Infogan: Interpretable representation learning by information maximizing generative adversarial nets.

Disentanglement in Difference: Directly Learning Semantically Disentangled Representations by Maximizing Inter-Factor Differences Infogan: Interpretable representation learning by information maximizing generative adversarial nets

Reference 8

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T05:54:33.809584Z digest=sha256:dae9a035edb2173508dfb40f15383a99faa0378970158eed705c62087396a399

Observation 89e5a313-c40f-43dc-a19a-945690f0f64f · outbound

This paper cites and Williams, C.

Disentanglement in Difference: Directly Learning Semantically Disentangled Representations by Maximizing Inter-Factor Differences and Williams, C

Reference 9

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Source-reported events for the cited work

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Observation bfb8021a-3076-48ad-9b9e-d17beec25b1d · outbound

This paper cites Demystifying Inter-Class Disentanglement.

Disentanglement in Difference: Directly Learning Semantically Disentangled Representations by Maximizing Inter-Factor Differences Demystifying Inter-Class Disentanglement

Reference 10

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Observation 2527fd9c-e406-4c01-98fe-18d78245fd53 · outbound

This paper cites and Hoshen, Y.

Disentanglement in Difference: Directly Learning Semantically Disentangled Representations by Maximizing Inter-Factor Differences and Hoshen, Y

Reference 11

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Observation eda6106e-676b-491e-8eb1-4dec29d3aeae · outbound

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Disentanglement in Difference: Directly Learning Semantically Disentangled Representations by Maximizing Inter-Factor Differences Unresolved cited work

Reference 12

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Observation e19c9e3d-c6dd-4f74-9620-4990709fdbcd · outbound

This paper cites P., Glorot, X., Botvinick, M.

Disentanglement in Difference: Directly Learning Semantically Disentangled Representations by Maximizing Inter-Factor Differences P., Glorot, X., Botvinick, M

Reference 13

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Observation 1796bca5-1738-45f7-8225-3c55d8e59b02 · outbound

This paper cites Towards a Definition of Disentangled Representations.

Disentanglement in Difference: Directly Learning Semantically Disentangled Representations by Maximizing Inter-Factor Differences Towards a Definition of Disentangled Representations

Reference 14

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Observation 1b7b4661-daaa-47de-b525-b6d592919a7c · outbound

This paper cites and Mnih, A.

Disentanglement in Difference: Directly Learning Semantically Disentangled Representations by Maximizing Inter-Factor Differences and Mnih, A

Reference 15

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Observation 2caa6de4-76da-4b38-9837-3749dfac81d0 · outbound

This paper cites Auto-Encoding Variational Bayes.

Disentanglement in Difference: Directly Learning Semantically Disentangled Representations by Maximizing Inter-Factor Differences Auto-Encoding Variational Bayes

Reference 16

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Observation 2e3be39d-853a-44db-81d9-17f28e04fee0 · outbound

This paper cites Variational Inference of Disentangled Latent Concepts from Unlabeled Observations.

Disentanglement in Difference: Directly Learning Semantically Disentangled Representations by Maximizing Inter-Factor Differences Variational Inference of Disentangled Latent Concepts from Unlabeled Observations

Reference 18

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Observation 8c197a8b-b192-457b-bb10-4fb241274a42 · outbound

This paper cites M., Ullman, T.

Disentanglement in Difference: Directly Learning Semantically Disentangled Representations by Maximizing Inter-Factor Differences M., Ullman, T

Reference 19

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Observation 80c1b29f-b377-413f-8290-f7f0217d606c · outbound

This paper cites Gradient-based learning applied to document recognition.

Disentanglement in Difference: Directly Learning Semantically Disentangled Representations by Maximizing Inter-Factor Differences Gradient-based learning applied to document recognition

Reference 20

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Observation 05de5287-7aa6-44a1-984b-49b415972cf4 · outbound

This paper cites Infogan-cr and modelcentrality: Self-supervised model training and selection for disentangling gans.

Disentanglement in Difference: Directly Learning Semantically Disentangled Representations by Maximizing Inter-Factor Differences Infogan-cr and modelcentrality: Self-supervised model training and selection for disentangling gans

Reference 21

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Observation b15e8023-ca05-483e-9315-c6683a5c0166 · outbound

This paper cites Challenging common assumptions in the unsupervised learning of disentangled representations.

Disentanglement in Difference: Directly Learning Semantically Disentangled Representations by Maximizing Inter-Factor Differences Challenging common assumptions in the unsupervised learning of disentangled representations

Reference 22

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Observation 46804536-d46c-4d31-a684-90181f163292 · outbound

This paper cites Disentangling Factors of Variation Using Few Labels.

Disentanglement in Difference: Directly Learning Semantically Disentangled Representations by Maximizing Inter-Factor Differences Disentangling Factors of Variation Using Few Labels

Reference 23

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Observation 72713ed8-6df9-4b19-a990-e27f98298a29 · outbound

This paper cites dsprites: Disentanglement testing sprites dataset.

Disentanglement in Difference: Directly Learning Semantically Disentangled Representations by Maximizing Inter-Factor Differences dsprites: Disentanglement testing sprites dataset

Reference 24

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Observation 2b7e3be0-4c55-4cdb-a990-ba43bd4c18bc · outbound

This paper cites Elements of causal inference: foundations and learning algorithms.

Disentanglement in Difference: Directly Learning Semantically Disentangled Representations by Maximizing Inter-Factor Differences Elements of causal inference: foundations and learning algorithms

Reference 25

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Observation 75b9c967-d538-43fd-9730-b8e8a9a65b48 · outbound

This paper cites Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks.

Disentanglement in Difference: Directly Learning Semantically Disentangled Representations by Maximizing Inter-Factor Differences Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

Reference 26

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Observation 924b156e-93a7-4cf8-a0f0-e0b69f525233 · outbound

This paper cites and Mozer, M.

Disentanglement in Difference: Directly Learning Semantically Disentangled Representations by Maximizing Inter-Factor Differences and Mozer, M

Reference 27

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Observation b04d2315-9cca-4c28-b2a1-02f8d1f81e1a · outbound

This paper cites Semilinear predictability minimization produces well-known feature detectors.

Disentanglement in Difference: Directly Learning Semantically Disentangled Representations by Maximizing Inter-Factor Differences Semilinear predictability minimization produces well-known feature detectors

Reference 28

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Observation 091d9e38-2c1c-4433-b9f5-311f7dc2da03 · outbound

This paper cites DynamicVAE: Decoupling Reconstruction Error and Disentangled Representation Learning.

Disentanglement in Difference: Directly Learning Semantically Disentangled Representations by Maximizing Inter-Factor Differences DynamicVAE: Decoupling Reconstruction Error and Disentangled Representation Learning

Reference 29

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Observation 1c47872d-cfc9-4610-adf0-bfdf320b48f5 · outbound

This paper cites Recent Advances in Autoencoder-Based Representation Learning.

Disentanglement in Difference: Directly Learning Semantically Disentangled Representations by Maximizing Inter-Factor Differences Recent Advances in Autoencoder-Based Representation Learning

Reference 30

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Observation b8c91283-bb7c-486d-8bb0-c4e48282bafc · outbound

This paper cites Are disentangled representations helpful for abstract visual reasoning? Advances in neural information processing systems, 32, 2019.

Disentanglement in Difference: Directly Learning Semantically Disentangled Representations by Maximizing Inter-Factor Differences Are disentangled representations helpful for abstract visual reasoning? Advances in neural information processing systems, 32, 2019

Reference 31

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Source-reported events for the cited work

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Observation 9f6ab369-6417-48b0-9de1-f097d50b4229 · outbound

This paper cites Information theoretical analysis of multivariate correlation.

Disentanglement in Difference: Directly Learning Semantically Disentangled Representations by Maximizing Inter-Factor Differences Information theoretical analysis of multivariate correlation

Reference 32

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Source-reported events for the cited work

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Disentanglement in Difference: Directly Learning Semantically Disentangled Representations by Maximizing Inter-Factor Differences Unresolved cited work

Reference 33

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Observation 10560adf-4862-4df4-96fb-8b6169753ac0 · outbound

This paper cites Evaluating the Disentanglement of Deep Generative Models through Manifold Topology.

Disentanglement in Difference: Directly Learning Semantically Disentangled Representations by Maximizing Inter-Factor Differences Evaluating the Disentanglement of Deep Generative Models through Manifold Topology

Reference 34

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Source-reported events for the cited work

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Observation 66d5f357-8bc4-4b3b-b279-86259dd482c4 · outbound

This paper cites Visual object networks: Image generation with disentangled 3d representations.

Disentanglement in Difference: Directly Learning Semantically Disentangled Representations by Maximizing Inter-Factor Differences Visual object networks: Image generation with disentangled 3d representations

Reference 35

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verified fuzzy
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Source-reported events for the cited work

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Observation 80897754-78a3-4fe2-a32a-10145355b352 · outbound

This paper cites Where and what? examining interpretable disentangled representations.

Disentanglement in Difference: Directly Learning Semantically Disentangled Representations by Maximizing Inter-Factor Differences Where and what? examining interpretable disentangled representations

Reference 36

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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.

source=arxiv_source observed=2026-08-09T05:54:34.185798Z digest=sha256:7f00538eade8327bb09123bab192809e43716972e585773cc42921808bc392bc

Observation 6a93ba45-4a20-4383-b15c-6d6b5c4d6783 · outbound

This paper cites write newline.

Disentanglement in Difference: Directly Learning Semantically Disentangled Representations by Maximizing Inter-Factor Differences write newline

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