Paper Citation Record · LEDGER

Deconstructing Denoising Diffusion Models for Self-Supervised Learning

As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 32 inbound Pith citation observations for arXiv:2401.14404.

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

pith.paper-citation-record.v1
2401.14404 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 32 of 32 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T06:02:15.761082Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T16:59:58.123921Z

Reference resolution

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External citation measurements

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Outbound references

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Pith citing papers

Observation 98d46857-a121-4f3d-8f0b-e54de2b896f6 · inbound

Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think cites this paper.

Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think Deconstructing Denoising Diffusion Models for Self-Supervised Learning

Reference 126

Resolution: verified exact; arxiv_id, observed 2026-05-12T15:09:37.088833Z

Source-reported events for the cited work

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Observation 6c8412a0-c0af-4c08-b3f2-c62d7b1e75d1 · inbound

An End-to-End Robust Point Cloud Semantic Segmentation Network with Single-Step Conditional Diffusion Models cites this paper.

An End-to-End Robust Point Cloud Semantic Segmentation Network with Single-Step Conditional Diffusion Models Deconstructing Denoising Diffusion Models for Self-Supervised Learning

Reference 7

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Track4Gen: Teaching Video Diffusion Models to Track Points Improves Video Generation cites this paper.

Track4Gen: Teaching Video Diffusion Models to Track Points Improves Video Generation Deconstructing Denoising Diffusion Models for Self-Supervised Learning

Reference 11

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Observation 5ed146b8-028f-4577-a6e2-05d74d7da021 · inbound

Visual Lexicon: Rich Image Features in Language Space cites this paper.

Visual Lexicon: Rich Image Features in Language Space Deconstructing Denoising Diffusion Models for Self-Supervised Learning

Reference 12

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GMem: A Modular Approach for Ultra-Efficient Generative Models cites this paper.

GMem: A Modular Approach for Ultra-Efficient Generative Models Deconstructing Denoising Diffusion Models for Self-Supervised Learning

Reference 5

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Observation c8ca871d-15cf-45a1-bae3-ea3b314ddf6c · inbound

SoftVQ-VAE: Efficient 1-Dimensional Continuous Tokenizer cites this paper.

SoftVQ-VAE: Efficient 1-Dimensional Continuous Tokenizer Deconstructing Denoising Diffusion Models for Self-Supervised Learning

Reference 14

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Observation d7d51be8-6c58-424d-809a-3ea35b81b605 · inbound

Improving Generative Pre-Training: An In-depth Study of Masked Image Modeling and Denoising Models cites this paper.

Improving Generative Pre-Training: An In-depth Study of Masked Image Modeling and Denoising Models Deconstructing Denoising Diffusion Models for Self-Supervised Learning

Reference 6

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Observation 9431aabb-2c9d-40f4-ba51-2ffabfb72db8 · inbound

Diffusion Models for Computational Neuroimaging: A Survey cites this paper.

Diffusion Models for Computational Neuroimaging: A Survey Deconstructing Denoising Diffusion Models for Self-Supervised Learning

Reference 6

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Flow Along the K-Amplitude for Generative Modeling cites this paper.

Flow Along the K-Amplitude for Generative Modeling Deconstructing Denoising Diffusion Models for Self-Supervised Learning

Reference 46

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X-Fusion: Introducing New Modality to Frozen Large Language Models cites this paper.

X-Fusion: Introducing New Modality to Frozen Large Language Models Deconstructing Denoising Diffusion Models for Self-Supervised Learning

Reference 78

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Observation 123cce83-0199-4f95-8166-3bbf0feb0f2c · inbound

Graffe: Graph Representation Learning via Diffusion Probabilistic Models cites this paper.

Graffe: Graph Representation Learning via Diffusion Probabilistic Models Deconstructing Denoising Diffusion Models for Self-Supervised Learning

Reference 16

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Observation fcecddff-5f85-4900-a5eb-7b84b16608a9 · inbound

Automated Learning of Semantic Embedding Representations for Diffusion Models cites this paper.

Automated Learning of Semantic Embedding Representations for Diffusion Models Deconstructing Denoising Diffusion Models for Self-Supervised Learning

Reference 22

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Observation 49400acf-bc5c-4177-a4cf-59a63f5a8586 · inbound

Image Classification Using a Diffusion Model as a Pre-Training Model cites this paper.

Image Classification Using a Diffusion Model as a Pre-Training Model Deconstructing Denoising Diffusion Models for Self-Supervised Learning

Reference 7

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Observation 3e47e6d0-928a-4940-830a-3f8c301c4d56 · inbound

Unlocking the Power of Diffusion Models in Sequential Recommendation: A Simple and Effective Approach cites this paper.

Unlocking the Power of Diffusion Models in Sequential Recommendation: A Simple and Effective Approach Deconstructing Denoising Diffusion Models for Self-Supervised Learning

Reference 2

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Observation 9effd71f-db1c-45e4-beac-2033421c43bb · inbound

Canonical Latent Representations in Conditional Diffusion Models cites this paper.

Canonical Latent Representations in Conditional Diffusion Models Deconstructing Denoising Diffusion Models for Self-Supervised Learning

Reference 10

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Observation 8edae33d-08d3-4730-bb8d-fc6fcbf02bf1 · inbound

Vision-Language-Vision Auto-Encoder: Scalable Knowledge Distillation from Diffusion Models cites this paper.

Vision-Language-Vision Auto-Encoder: Scalable Knowledge Distillation from Diffusion Models Deconstructing Denoising Diffusion Models for Self-Supervised Learning

Reference 15

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Observation ec37f750-0f28-4666-9682-a4b763fc6545 · inbound

Visualising relativistic effects in redshift space distortions of large scale structure cites this paper.

Visualising relativistic effects in redshift space distortions of large scale structure Deconstructing Denoising Diffusion Models for Self-Supervised Learning

Reference 2020

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Observation 095642e9-b2f5-47e8-8e79-ec96c9570365 · inbound

Real-Time 3D Vision-Language Embedding Mapping cites this paper.

Real-Time 3D Vision-Language Embedding Mapping Deconstructing Denoising Diffusion Models for Self-Supervised Learning

Reference 5

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Observation 1a260cd4-33ea-47ce-8f77-50b4789b6c0a · inbound

Real-Time 3D Vision-Language Embedding Mapping cites this paper.

Real-Time 3D Vision-Language Embedding Mapping Deconstructing Denoising Diffusion Models for Self-Supervised Learning

Reference 2025

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Observation dab488d4-2ac8-41d6-96f2-e9c691ae22d2 · inbound

CoGenCast: A Coupled Autoregressive-Flow Generative Framework for Time Series Forecasting cites this paper.

CoGenCast: A Coupled Autoregressive-Flow Generative Framework for Time Series Forecasting Deconstructing Denoising Diffusion Models for Self-Supervised Learning

Reference 2025

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Xray-Visual Models: Scaling Vision models on Industry Scale Data cites this paper.

Xray-Visual Models: Scaling Vision models on Industry Scale Data Deconstructing Denoising Diffusion Models for Self-Supervised Learning

Reference 9

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Revisiting Autoregressive Models for Generative Image Classification cites this paper.

Revisiting Autoregressive Models for Generative Image Classification Deconstructing Denoising Diffusion Models for Self-Supervised Learning

Reference 6

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VFM$^{4}$SDG: Unveiling the Power of VFMs for Single-Domain Generalized Object Detection cites this paper.

VFM$^{4}$SDG: Unveiling the Power of VFMs for Single-Domain Generalized Object Detection Deconstructing Denoising Diffusion Models for Self-Supervised Learning

Reference 43

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Observation 454317d4-9211-40fd-baf8-4dcd9e413d00 · inbound

VFM$^{4}$SDG: Unveiling the Power of VFMs for Single-Domain Generalized Object Detection cites this paper.

VFM$^{4}$SDG: Unveiling the Power of VFMs for Single-Domain Generalized Object Detection Deconstructing Denoising Diffusion Models for Self-Supervised Learning

Reference 43

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Source-reported events for the cited work

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Observation 355ff940-1c40-4809-80dd-856333aeb1e2 · inbound

The two clocks and the innovation window: When and how generative models learn rules cites this paper.

The two clocks and the innovation window: When and how generative models learn rules Deconstructing Denoising Diffusion Models for Self-Supervised Learning

Reference 39

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Observation b6b0dbda-32ec-4eb1-a512-9aeefe016301 · inbound

Semantic Generative Tuning for Unified Multimodal Models cites this paper.

Semantic Generative Tuning for Unified Multimodal Models Deconstructing Denoising Diffusion Models for Self-Supervised Learning

Reference 7

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Observation 1ef9940f-ad48-4702-9956-cea6d8752e70 · inbound

Semantic Generative Tuning for Unified Multimodal Models cites this paper.

Semantic Generative Tuning for Unified Multimodal Models Deconstructing Denoising Diffusion Models for Self-Supervised Learning

Reference 7

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 4c120460-cab3-4e0f-9f46-4672cec761d1 · inbound

Improving Visual Representation Alignment Generation with GRPO cites this paper.

Improving Visual Representation Alignment Generation with GRPO Deconstructing Denoising Diffusion Models for Self-Supervised Learning

Reference 4

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Observation e21c703e-ec96-4275-b6f3-f16c7b796960 · inbound

DiffusionBench: On Holistic Evaluation of Diffusion Transformers cites this paper.

DiffusionBench: On Holistic Evaluation of Diffusion Transformers Deconstructing Denoising Diffusion Models for Self-Supervised Learning

Reference 107

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T16:59:58.125550Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 634428de-0cd4-4e99-9799-1da7c3535d58 · inbound

T2LDM++: A Self-Conditioned Representation Guided Diffusion Model for Realistic Text-to-LiDAR Scene Generation cites this paper.

T2LDM++: A Self-Conditioned Representation Guided Diffusion Model for Realistic Text-to-LiDAR Scene Generation Deconstructing Denoising Diffusion Models for Self-Supervised Learning

Reference 61

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Source-reported events for the cited work

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Observation 663d5918-ae0d-43fc-b0ce-7082bd31f7d4 · inbound

T2LDM++: A Self-Conditioned Representation Guided Diffusion Model for Realistic Text-to-LiDAR Scene Generation cites this paper.

T2LDM++: A Self-Conditioned Representation Guided Diffusion Model for Realistic Text-to-LiDAR Scene Generation Deconstructing Denoising Diffusion Models for Self-Supervised Learning

Reference 61

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Exploring the Alignment of Generation and Understanding in Protein Structure Modeling cites this paper.

Exploring the Alignment of Generation and Understanding in Protein Structure Modeling Deconstructing Denoising Diffusion Models for Self-Supervised Learning

Reference 6

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