An Information-Theoretic Regularizer for Lossy Neural Image Compression | Paper Citation Record

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

An Information-Theoretic Regularizer for Lossy Neural Image Compression Coding theorems for a discrete source with a fidelity criterion,

Reference 1

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Observation 3e5f1b45-20f3-4987-9ef7-bc53a8129b14 · outbound

This paper cites Sayood, Introduction to data compression.

An Information-Theoretic Regularizer for Lossy Neural Image Compression Sayood, Introduction to data compression

Reference 2

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Observation afea31f0-ad71-4f91-a83a-c782292ca284 · outbound

This paper cites An introduction to neural data compression,.

An Information-Theoretic Regularizer for Lossy Neural Image Compression An introduction to neural data compression,

Reference 3

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Observation 4b7671de-533a-4c55-9cd1-f358f524f5dc · outbound

This paper cites Variational image compression with a scale hyperprior.

An Information-Theoretic Regularizer for Lossy Neural Image Compression Variational image compression with a scale hyperprior

Reference 4

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This paper cites Rate-distortion theory,.

An Information-Theoretic Regularizer for Lossy Neural Image Compression Rate-distortion theory,

Reference 5

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This paper cites Theoretical foundations of transform coding,.

An Information-Theoretic Regularizer for Lossy Neural Image Compression Theoretical foundations of transform coding,

Reference 6

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Observation 1cce6654-9fd3-4b66-8111-8a51a26675d5 · outbound

This paper cites End-to-end Optimized Image Compression.

An Information-Theoretic Regularizer for Lossy Neural Image Compression End-to-end Optimized Image Compression

Reference 7

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Observation eb0f422a-905e-44a3-b782-39ef5dcf3051 · outbound

This paper cites Joint autoregres- sive and hierarchical priors for learned image compression,.

An Information-Theoretic Regularizer for Lossy Neural Image Compression Joint autoregres- sive and hierarchical priors for learned image compression,

Reference 8

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Observation 4f7a6e2e-2419-4299-99da-66314b342c65 · outbound

This paper cites Channel-wise autoregressive en- tropy models for learned image compression,.

An Information-Theoretic Regularizer for Lossy Neural Image Compression Channel-wise autoregressive en- tropy models for learned image compression,

Reference 9

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Observation b8cacfeb-5ccb-444e-b04e-cd3255b83071 · outbound

This paper cites Learned image compression with discretized gaussian mixture like- lihoods and attention modules,.

An Information-Theoretic Regularizer for Lossy Neural Image Compression Learned image compression with discretized gaussian mixture like- lihoods and attention modules,

Reference 10

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Observation d5b43cd6-5a81-48f2-8279-d4ac595e505d · outbound

This paper cites Checker- board context model for efficient learned image compres- sion,.

An Information-Theoretic Regularizer for Lossy Neural Image Compression Checker- board context model for efficient learned image compres- sion,

Reference 11

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Observation 1e4fdb9a-bb53-42b0-8061-ee2acba7c860 · outbound

This paper cites Mlic++: Linear complexity multi- reference entropy modeling for learned image compression,.

An Information-Theoretic Regularizer for Lossy Neural Image Compression Mlic++: Linear complexity multi- reference entropy modeling for learned image compression,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:24:32.215208Z

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Observation fe6ce332-c95b-4693-9e4c-29878a88d6c7 · outbound

This paper cites MambaVC: Learned Visual Compression with Selective State Spaces.

An Information-Theoretic Regularizer for Lossy Neural Image Compression MambaVC: Learned Visual Compression with Selective State Spaces

Reference 13

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Observation 69bce4c1-6998-4695-9de1-8b9c9a120ef9 · outbound

This paper cites On Uniform Scalar Quantization for Learned Image Compression.

An Information-Theoretic Regularizer for Lossy Neural Image Compression On Uniform Scalar Quantization for Learned Image Compression

Reference 14

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This paper cites Soft-to-hard vector quantization for end-to-end learning compressible represen- tations,.

An Information-Theoretic Regularizer for Lossy Neural Image Compression Soft-to-hard vector quantization for end-to-end learning compressible represen- tations,

Reference 15

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Observation 6a587808-e9b9-426a-a372-b1e90d03bcc9 · outbound

This paper cites Improving inference for neural image compression,.

An Information-Theoretic Regularizer for Lossy Neural Image Compression Improving inference for neural image compression,

Reference 16

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This paper cites Universally quantized neural compression,.

An Information-Theoretic Regularizer for Lossy Neural Image Compression Universally quantized neural compression,

Reference 17

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Observation 27130d28-d1b4-4e86-b7b6-e722b1b83b66 · outbound

This paper cites Soft then hard: Rethinking the quantization in neural image compression,.

An Information-Theoretic Regularizer for Lossy Neural Image Compression Soft then hard: Rethinking the quantization in neural image compression,

Reference 18

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Observation 803df6b3-881f-49cc-86a4-cbe6c092aacb · outbound

This paper cites An introduction to varia- tional autoencoders,.

An Information-Theoretic Regularizer for Lossy Neural Image Compression An introduction to varia- tional autoencoders,

Reference 19

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Observation b236c68e-54e1-4dad-8712-2a16302398f9 · outbound

This paper cites Ad- versarially regularized autoencoders,.

An Information-Theoretic Regularizer for Lossy Neural Image Compression Ad- versarially regularized autoencoders,

Reference 20

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This paper cites Learning autoencoders with relational regularization,.

An Information-Theoretic Regularizer for Lossy Neural Image Compression Learning autoencoders with relational regularization,

Reference 21

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This paper cites Vector quantization-based regulariza- tion for autoencoders,.

An Information-Theoretic Regularizer for Lossy Neural Image Compression Vector quantization-based regulariza- tion for autoencoders,

Reference 22

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Observation 5a341ead-e6ff-43e3-9bf6-cef95c40e6de · outbound

This paper cites Supervised autoen- coders: Improving generalization performance with unsuper- vised regularizers,.

An Information-Theoretic Regularizer for Lossy Neural Image Compression Supervised autoen- coders: Improving generalization performance with unsuper- vised regularizers,

Reference 23

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Observation dd99738d-22aa-4927-8517-f536d4b7725d · outbound

This paper cites Constrained generation of se- mantically valid graphs via regularizing variational autoen- coders,.

An Information-Theoretic Regularizer for Lossy Neural Image Compression Constrained generation of se- mantically valid graphs via regularizing variational autoen- coders,

Reference 24

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This paper cites Consistency regularization for variational auto-encoders,.

An Information-Theoretic Regularizer for Lossy Neural Image Compression Consistency regularization for variational auto-encoders,

Reference 25

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Observation 5f8c9e77-e7b8-4742-ba24-72cf7ba1f047 · outbound

This paper cites Ochoa-Dominguez and K.

An Information-Theoretic Regularizer for Lossy Neural Image Compression Ochoa-Dominguez and K

Reference 26

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Observation 91ba284b-f808-4fa3-806f-afd0fddcd7e8 · outbound

This paper cites Variable Rate Image Compression with Recurrent Neural Networks.

An Information-Theoretic Regularizer for Lossy Neural Image Compression Variable Rate Image Compression with Recurrent Neural Networks

Reference 27

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Observation 49ac70ef-0379-4538-8fb1-c94f18d356fe · outbound

This paper cites Non-local Attention Optimized Deep Image Compression.

An Information-Theoretic Regularizer for Lossy Neural Image Compression Non-local Attention Optimized Deep Image Compression

Reference 28

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Observation d0abac7b-4ea6-447f-a04d-c561b4d107d4 · outbound

This paper cites Transformer-based Image Compression.

An Information-Theoretic Regularizer for Lossy Neural Image Compression Transformer-based Image Compression

Reference 29

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Observation fa94c194-8ba4-41ab-b70e-1116175eeebf · outbound

This paper cites Learned image compression with mixed transformer-cnn architectures,.

An Information-Theoretic Regularizer for Lossy Neural Image Compression Learned image compression with mixed transformer-cnn architectures,

Reference 30

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This paper cites End-to-end opti- mized versatile image compression with wavelet-like trans- form,.

An Information-Theoretic Regularizer for Lossy Neural Image Compression End-to-end opti- mized versatile image compression with wavelet-like trans- form,

Reference 31

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This paper cites Lvqac: Lattice vector quantiza- tion coupled with spatially adaptive companding for effi- cient learned image compression,.

An Information-Theoretic Regularizer for Lossy Neural Image Compression Lvqac: Lattice vector quantiza- tion coupled with spatially adaptive companding for effi- cient learned image compression,

Reference 32

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This paper cites Nvtc: Nonlinear vector transform coding,.

An Information-Theoretic Regularizer for Lossy Neural Image Compression Nvtc: Nonlinear vector transform coding,

Reference 33

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Observation 93c6cfd6-dde7-433e-854c-eab5150afeb3 · outbound

This paper cites Trellis-coded quantization for end-to-end learned image compression,.

An Information-Theoretic Regularizer for Lossy Neural Image Compression Trellis-coded quantization for end-to-end learned image compression,

Reference 34

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Observation f05c32de-9fed-4b34-be85-5bbb144f9d2a · outbound

This paper cites Nlic: Non- uniform quantization based learned image compression,.

An Information-Theoretic Regularizer for Lossy Neural Image Compression Nlic: Non- uniform quantization based learned image compression,

Reference 35

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This paper cites Causal contextual prediction for learned image compression,.

An Information-Theoretic Regularizer for Lossy Neural Image Compression Causal contextual prediction for learned image compression,

Reference 36

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Observation eba4b728-54ba-4e79-b4ce-283b9f856248 · outbound

This paper cites Unified multivariate gaussian mixture for efficient neural image com- pression,.

An Information-Theoretic Regularizer for Lossy Neural Image Compression Unified multivariate gaussian mixture for efficient neural image com- pression,

Reference 37

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Observation 9c6579fa-7afd-4638-9c67-82da2f1769ca · outbound

This paper cites Learned image compression with gaussian-laplacian-logistic mixture model and concatenated residual modules,.

An Information-Theoretic Regularizer for Lossy Neural Image Compression Learned image compression with gaussian-laplacian-logistic mixture model and concatenated residual modules,

Reference 38

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This paper cites Coarse-to-fine hyper-prior modeling for learned image compression,.

An Information-Theoretic Regularizer for Lossy Neural Image Compression Coarse-to-fine hyper-prior modeling for learned image compression,

Reference 39

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This paper cites Lossy Image Compression with Compressive Autoencoders.

An Information-Theoretic Regularizer for Lossy Neural Image Compression Lossy Image Compression with Compressive Autoencoders

Reference 40

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This paper cites Deep learning,.

An Information-Theoretic Regularizer for Lossy Neural Image Compression Deep learning,

Reference 41

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This paper cites Generative adversarial nets,.

An Information-Theoretic Regularizer for Lossy Neural Image Compression Generative adversarial nets,

Reference 42

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Observation 7d10553b-03e5-4ab1-acb3-57594da99366 · outbound

This paper cites Elic: Efficient learned image compression with unevenly grouped space-channel contextual adaptive coding,.

An Information-Theoretic Regularizer for Lossy Neural Image Compression Elic: Efficient learned image compression with unevenly grouped space-channel contextual adaptive coding,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:24:32.015693Z

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Observation e60d0adc-da4e-45fd-b338-4d5908241b45 · outbound

This paper cites CompressAI: a PyTorch library and evaluation platform for end-to-end compression research.

An Information-Theoretic Regularizer for Lossy Neural Image Compression CompressAI: a PyTorch library and evaluation platform for end-to-end compression research

Reference 44

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This paper cites Unofficial elic.

An Information-Theoretic Regularizer for Lossy Neural Image Compression Unofficial elic

Reference 45

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Observation 36f42373-67d3-4c83-8248-de3c03ed40fc · outbound

This paper cites A Unified End-to-End Framework for Efficient Deep Image Compression.

An Information-Theoretic Regularizer for Lossy Neural Image Compression A Unified End-to-End Framework for Efficient Deep Image Compression

Reference 46

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This paper cites Adam: A Method for Stochastic Optimization.

An Information-Theoretic Regularizer for Lossy Neural Image Compression Adam: A Method for Stochastic Optimization

Reference 47

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This paper cites Kodak lossless true color image suite (photocd pcd0992),.

An Information-Theoretic Regularizer for Lossy Neural Image Compression Kodak lossless true color image suite (photocd pcd0992),

Reference 48

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This paper cites 6th Challenge on Learned Image Compression.

An Information-Theoretic Regularizer for Lossy Neural Image Compression 6th Challenge on Learned Image Compression

Reference 49

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Observation df06bdc5-7db2-45fd-a845-0f5be5fd2994 · outbound

This paper cites Testimages: a large-scale archive for testing visual devices and basic image process- ing algorithms.,.

An Information-Theoretic Regularizer for Lossy Neural Image Compression Testimages: a large-scale archive for testing visual devices and basic image process- ing algorithms.,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:24:31.983464Z

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Observation 13f5ae2d-d04a-40fd-b4b8-1fdbf5bca16d · outbound

This paper cites Few-Shot Domain Adaptation for Learned Image Compression.

An Information-Theoretic Regularizer for Lossy Neural Image Compression Few-Shot Domain Adaptation for Learned Image Compression

Reference 51

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Observation 7d4a6694-d49d-4339-8e36-d1192e04b274 · outbound

This paper cites Dynamic low-rank instance adaptation for universal neural image compression,.

An Information-Theoretic Regularizer for Lossy Neural Image Compression Dynamic low-rank instance adaptation for universal neural image compression,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:24:31.974639Z

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Observation 0ce51e5d-4d9a-4255-a6dd-b69239b6f108 · outbound

This paper cites Implicit trans- former network for screen content image continuous super- resolution,.

An Information-Theoretic Regularizer for Lossy Neural Image Compression Implicit trans- former network for screen content image continuous super- resolution,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:24:31.966388Z

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Observation 6d9dc19d-a2b7-46b0-ae77-d07672f29695 · outbound

This paper cites Uni- fied blind quality assessment of compressed natural, graphic, and screen content images,.

An Information-Theoretic Regularizer for Lossy Neural Image Compression Uni- fied blind quality assessment of compressed natural, graphic, and screen content images,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:24:31.958266Z

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Observation 6602d70c-5530-489b-801e-f0f7263e108d · outbound

This paper cites Bracs: A dataset for breast carcinoma subtyping in h&e histology images,.

An Information-Theoretic Regularizer for Lossy Neural Image Compression Bracs: A dataset for breast carcinoma subtyping in h&e histology images,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:24:31.949967Z

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Observation 0929d1ab-c273-4094-876b-d65e4130c3d2 · outbound

This paper cites Xception: Deep learning with depthwise sepa- rable convolutions,.

An Information-Theoretic Regularizer for Lossy Neural Image Compression Xception: Deep learning with depthwise sepa- rable convolutions,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:24:31.941536Z

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Observation 3beb61ca-b19c-4af8-8933-8ed16272ce79 · outbound

This paper cites Calculation of average PSNR differences between RD-curves,.

An Information-Theoretic Regularizer for Lossy Neural Image Compression Calculation of average PSNR differences between RD-curves,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:24:31.932641Z

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Observation e5133c85-8683-4242-8674-c70943fd8b9d · outbound

This paper cites an unresolved cited work.

An Information-Theoretic Regularizer for Lossy Neural Image Compression Unresolved cited work

Reference 58

Resolution: unresolved; raw_fallback, observed 2026-08-12T14:24:31.923770Z

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

Observation fe67e47c-7269-4ae9-a607-154bca821be3 · outbound

This paper cites an unresolved cited work.

An Information-Theoretic Regularizer for Lossy Neural Image Compression Unresolved cited work

Reference 59

Resolution: unresolved; raw_fallback, observed 2026-08-12T14:24:31.914972Z

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 51de2372-e23d-40b5-b7c9-fc0853d28889 · outbound

This paper cites Four bit-rate points, i.e., λ ∈ {0.0018, 0.0035, 0.0067, 0.0130} are trained with 2 × 106 steps.

An Information-Theoretic Regularizer for Lossy Neural Image Compression Four bit-rate points, i.e., λ ∈ {0.0018, 0.0035, 0.0067, 0.0130} are trained with 2 × 106 steps

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:24:31.906835Z

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Observation b63fdd43-43f5-4656-93b3-beeca2de403e · outbound

This paper cites The details are depicted in Fig.7.

An Information-Theoretic Regularizer for Lossy Neural Image Compression The details are depicted in Fig.7

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:24:31.898167Z

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

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