Paper Citation Record · LEDGER

Preventing Local Pitfalls in Vector Quantization via Optimal Transport

As of 19 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 1 inbound Pith citation observation for arXiv:2412.15195.

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

pith.paper-citation-record.v1
2412.15195 v1

Coverage vector

measured 48 of 48 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T11:38:29.866761Z

measured 49 of 49 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:34:57.395098Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T17:34:57.475555Z

Reference resolution

48 of 48 outbound references displayed

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External citation measurements

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Outbound references

Observation 005f7b06-7f2f-4232-95df-5ce8eb4cb69d · outbound

This paper cites GPT-4 Technical Report.

Preventing Local Pitfalls in Vector Quantization via Optimal Transport GPT-4 Technical Report

Reference 1

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Observation 62abc3dc-9de1-4ee5-866c-f4fbe89e872a · outbound

This paper cites Self-labelling via simultaneous clustering and representation learning.

Preventing Local Pitfalls in Vector Quantization via Optimal Transport Self-labelling via simultaneous clustering and representation learning

Reference 2

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Observation 62def7ae-c21e-479f-b2e8-46a8f110d497 · outbound

This paper cites Sequential modeling enables scalable learn- ing for large vision models.

Preventing Local Pitfalls in Vector Quantization via Optimal Transport Sequential modeling enables scalable learn- ing for large vision models

Reference 3

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Observation fd3e028a-1496-4143-9e89-9b137df5995f · outbound

This paper cites Beit: Bert pre-training of image transformers.

Preventing Local Pitfalls in Vector Quantization via Optimal Transport Beit: Bert pre-training of image transformers

Reference 4

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Observation 54b758ab-a975-4699-aaea-c5828ce0e8aa · outbound

This paper cites Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation.

Preventing Local Pitfalls in Vector Quantization via Optimal Transport Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation

Reference 5

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Observation 70c8e765-5f2a-415b-a3cc-651b6054a33c · outbound

This paper cites Language Models are Few-Shot Learners.

Preventing Local Pitfalls in Vector Quantization via Optimal Transport Language Models are Few-Shot Learners

Reference 6

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Observation f4ea4be3-7750-4b0e-8a3b-efd4db61fa96 · outbound

This paper cites Efficient-vqgan: To- wards high-resolution image generation with efficient vision transformers.

Preventing Local Pitfalls in Vector Quantization via Optimal Transport Efficient-vqgan: To- wards high-resolution image generation with efficient vision transformers

Reference 7

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Observation 0468bab9-a0c0-444a-b16c-ab3bd992e6fe · outbound

This paper cites Deep clustering for unsupervised learning of visual features.

Preventing Local Pitfalls in Vector Quantization via Optimal Transport Deep clustering for unsupervised learning of visual features

Reference 8

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Observation 4d669d72-f9c7-4dd1-8238-568af08d2a07 · outbound

This paper cites Unsupervised learn- ing of visual features by contrasting cluster assignments.

Preventing Local Pitfalls in Vector Quantization via Optimal Transport Unsupervised learn- ing of visual features by contrasting cluster assignments

Reference 9

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Observation 383f231a-b8f0-4f66-8937-79231a6441ba · outbound

This paper cites Maskgit: Masked generative image transformer.

Preventing Local Pitfalls in Vector Quantization via Optimal Transport Maskgit: Masked generative image transformer

Reference 10

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Observation d69d2619-52d0-4ff3-bfba-54fb32075508 · outbound

This paper cites Sinkhorn distances: Lightspeed computation of optimal transport.

Preventing Local Pitfalls in Vector Quantization via Optimal Transport Sinkhorn distances: Lightspeed computation of optimal transport

Reference 11

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Observation fbc40932-f026-4f33-9230-b8fa236e7b79 · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

Preventing Local Pitfalls in Vector Quantization via Optimal Transport Imagenet: A large-scale hierarchical image database

Reference 12

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Observation f509b4f1-2f6c-4a7d-a137-ebd84bd57bdd · outbound

This paper cites Exact penalty methods.

Preventing Local Pitfalls in Vector Quantization via Optimal Transport Exact penalty methods

Reference 13

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Observation 1e267a0a-b3b9-4b62-b32a-27613362f8c9 · outbound

This paper cites Generating images with perceptual similarity metrics based on deep networks.

Preventing Local Pitfalls in Vector Quantization via Optimal Transport Generating images with perceptual similarity metrics based on deep networks

Reference 14

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Observation eac00ba0-defb-4665-90cf-03073587d001 · outbound

This paper cites Taming transformers for high-resolution image synthesis.

Preventing Local Pitfalls in Vector Quantization via Optimal Transport Taming transformers for high-resolution image synthesis

Reference 15

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Observation 3208054b-d301-4a98-933c-c688b99bbdad · outbound

This paper cites Making llama see and draw with seed tokenizer.

Preventing Local Pitfalls in Vector Quantization via Optimal Transport Making llama see and draw with seed tokenizer

Reference 16

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Observation e786fdc2-6b7c-4652-b24d-993f711dd8fc · outbound

This paper cites Generative adversarial nets.

Preventing Local Pitfalls in Vector Quantization via Optimal Transport Generative adversarial nets

Reference 17

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Observation 14fee287-7d42-458a-ba9c-0b187e2bf4ac · outbound

This paper cites Exact penalty functions in nonlinear programming.

Preventing Local Pitfalls in Vector Quantization via Optimal Transport Exact penalty functions in nonlinear programming

Reference 18

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Observation 867783d1-20cc-4a02-817c-3ce46c58d476 · outbound

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

Preventing Local Pitfalls in Vector Quantization via Optimal Transport Gans trained by a two time-scale update rule converge to a local nash equilib- rium

Reference 19

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Observation 63f77a92-5195-47f1-b6df-fffe2a00d316 · outbound

This paper cites Reducing the dimensionality of data with neural networks.

Preventing Local Pitfalls in Vector Quantization via Optimal Transport Reducing the dimensionality of data with neural networks

Reference 20

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Observation 0fd56170-c59d-46fb-844a-9ccae2c2ef10 · outbound

This paper cites Straightening out the straight-through estimator: Over- coming optimization challenges in vector quantized net- works.

Preventing Local Pitfalls in Vector Quantization via Optimal Transport Straightening out the straight-through estimator: Over- coming optimization challenges in vector quantized net- works

Reference 21

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Observation 88f7edcc-6410-419a-b220-fef531d2ff5b · outbound

This paper cites Batch normalization: Accelerating deep network training by reducing internal co- variate shift.

Preventing Local Pitfalls in Vector Quantization via Optimal Transport Batch normalization: Accelerating deep network training by reducing internal co- variate shift

Reference 22

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Observation 9235fc3c-c7b4-41b5-8ab8-a2b1c4526330 · outbound

This paper cites Image-to-image translation with conditional adver- sarial networks.

Preventing Local Pitfalls in Vector Quantization via Optimal Transport Image-to-image translation with conditional adver- sarial networks

Reference 23

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Observation 875c58c7-8359-41c3-9047-b1bc795c512f · outbound

This paper cites Unified language-vision pretraining in llm with dynamic discrete visual tokenization.

Preventing Local Pitfalls in Vector Quantization via Optimal Transport Unified language-vision pretraining in llm with dynamic discrete visual tokenization

Reference 24

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Observation bf6520a0-2c18-4154-97db-ae2f50ad2227 · outbound

This paper cites Perceptual losses for real-time style transfer and super-resolution.

Preventing Local Pitfalls in Vector Quantization via Optimal Transport Perceptual losses for real-time style transfer and super-resolution

Reference 25

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Observation 55ff1b28-e2c7-4ac4-a5c0-22af2f40933e · outbound

This paper cites Auto-Encoding Variational Bayes.

Preventing Local Pitfalls in Vector Quantization via Optimal Transport Auto-Encoding Variational Bayes

Reference 26

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Observation 54a92d9c-046a-48ba-8c67-0209e20ef680 · outbound

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

Preventing Local Pitfalls in Vector Quantization via Optimal Transport Learning multiple layers of features from tiny images

Reference 27

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Observation 94c4744e-841a-458a-b0e6-55459a792e7f · outbound

This paper cites Autoencoding beyond pixels using a learned similarity metric.

Preventing Local Pitfalls in Vector Quantization via Optimal Transport Autoencoding beyond pixels using a learned similarity metric

Reference 28

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Observation f569fb17-220b-47f2-85f1-1f1ce5a0dd90 · outbound

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Preventing Local Pitfalls in Vector Quantization via Optimal Transport Gradient-based learning applied to document recog- nition

Reference 29

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Observation e4f8b84d-5c73-41aa-ad06-110637661c58 · outbound

This paper cites Autoregressive image generation using residual quantization.

Preventing Local Pitfalls in Vector Quantization via Optimal Transport Autoregressive image generation using residual quantization

Reference 30

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Observation c9571dc0-fbf6-4d48-9bfe-b3971917ff23 · outbound

This paper cites Decoupled weight decay regularization.

Preventing Local Pitfalls in Vector Quantization via Optimal Transport Decoupled weight decay regularization

Reference 31

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Preventing Local Pitfalls in Vector Quantization via Optimal Transport Pytorch: An imperative style, high-performance deep learning library

Reference 32

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Observation 452408c2-f502-4367-afa5-bd20491d13b0 · outbound

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

Preventing Local Pitfalls in Vector Quantization via Optimal Transport Gen- erating diverse high-fidelity images with vq-vae-2

Reference 33

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Observation 518865f1-f544-462c-b394-717ff5cd747b · outbound

This paper cites High-resolution image syn- thesis with latent diffusion models.

Preventing Local Pitfalls in Vector Quantization via Optimal Transport High-resolution image syn- thesis with latent diffusion models

Reference 34

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Observation b5df55a1-cc40-44a0-8a2d-df1dd56c8f39 · outbound

This paper cites Learning internal representations by error prop- agation, parallel distributed processing, explorations in the microstructure of cognition, ed.

Preventing Local Pitfalls in Vector Quantization via Optimal Transport Learning internal representations by error prop- agation, parallel distributed processing, explorations in the microstructure of cognition, ed

Reference 35

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Observation 78568692-e869-4724-b004-be6b87c9ee7f · outbound

This paper cites Coding theorems for a discrete source with a fidelity criterion.

Preventing Local Pitfalls in Vector Quantization via Optimal Transport Coding theorems for a discrete source with a fidelity criterion

Reference 36

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Observation 5135bf85-df26-4f04-a9be-0a30a2ac28ad · outbound

This paper cites Super-convergence: Very fast training of neural networks using large learning rates.

Preventing Local Pitfalls in Vector Quantization via Optimal Transport Super-convergence: Very fast training of neural networks using large learning rates

Reference 37

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Observation cc1da061-4ac0-40ac-b099-70dbdb6d2921 · outbound

This paper cites Visual autoregressive modeling: Scalable image generation via next-scale prediction.

Preventing Local Pitfalls in Vector Quantization via Optimal Transport Visual autoregressive modeling: Scalable image generation via next-scale prediction

Reference 38

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Observation 36dca8bf-fbde-46a4-9745-d0b80cd303e9 · outbound

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Preventing Local Pitfalls in Vector Quantization via Optimal Transport LLaMA: Open and Efficient Foundation Language Models

Reference 39

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Preventing Local Pitfalls in Vector Quantization via Optimal Transport Neural discrete representation learning

Reference 40

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Observation 83bdd3b6-2536-4a82-b2fb-b5a2f7f37567 · outbound

This paper cites Image quality assessment: from error visibility to structural similarity.

Preventing Local Pitfalls in Vector Quantization via Optimal Transport Image quality assessment: from error visibility to structural similarity

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:38:30.533626Z

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Observation 1cca3341-cce5-4073-b89a-14d715a096a5 · outbound

This paper cites MaskBit: Embedding-free Image Generation via Bit Tokens.

Preventing Local Pitfalls in Vector Quantization via Optimal Transport MaskBit: Embedding-free Image Generation via Bit Tokens

Reference 42

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Source-reported events for the cited work

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Observation 506eb382-bc78-41c4-8c44-9d00ab8e30cd · outbound

This paper cites Vector-quantized image modeling with improved vqgan.

Preventing Local Pitfalls in Vector Quantization via Optimal Transport Vector-quantized image modeling with improved vqgan

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:38:30.506825Z

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Observation 6cedb479-3c0b-4c0b-ae93-6276014a2c4d · outbound

This paper cites Language model beats diffusion-tokenizer is key to visual generation.

Preventing Local Pitfalls in Vector Quantization via Optimal Transport Language model beats diffusion-tokenizer is key to visual generation

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:38:30.423891Z

Source-reported events for the cited work

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Observation d1961332-2bac-4989-a810-059d01fc5b6f · outbound

This paper cites An image is worth 32 tokens for reconstruction and generation.

Preventing Local Pitfalls in Vector Quantization via Optimal Transport An image is worth 32 tokens for reconstruction and generation

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:38:30.331067Z

Source-reported events for the cited work

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Observation f03c02a0-d781-46a0-b2a0-bd7486fc8f00 · outbound

This paper cites The unreasonable effectiveness of deep features as a perceptual metric.

Preventing Local Pitfalls in Vector Quantization via Optimal Transport The unreasonable effectiveness of deep features as a perceptual metric

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-11T11:38:29.825923Z

Source-reported events for the cited work

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Observation 084e9fda-b869-43b4-8052-e926842bbb1f · outbound

This paper cites Movq: Modulating quantized vectors for high- fidelity image generation.

Preventing Local Pitfalls in Vector Quantization via Optimal Transport Movq: Modulating quantized vectors for high- fidelity image generation

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:38:30.254872Z

Source-reported events for the cited work

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Observation 582e0f43-eef2-45f7-99cc-7fc8d2641c8c · outbound

This paper cites Scaling the Codebook Size of VQGAN to 100,000 with a Utilization Rate of 99%.

Preventing Local Pitfalls in Vector Quantization via Optimal Transport Scaling the Codebook Size of VQGAN to 100,000 with a Utilization Rate of 99%

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-11T11:38:29.866761Z

Source-reported events for the cited work

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Pith citing papers

Observation 4ee414ec-de86-4feb-b144-a30196dd9f79 · inbound

Quantize-then-Rectify: Efficient VQ-VAE Training cites this paper.

Quantize-then-Rectify: Efficient VQ-VAE Training Preventing Local Pitfalls in Vector Quantization via Optimal Transport

Reference 41

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:34:57.483082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.