Paper Citation Record · LEDGER

Efficient training for compact compression models via sequential distillation

As of 8 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 1 inbound Pith citation observation for arXiv:2601.05639.

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

pith.paper-citation-record.v1
2601.05639 v2

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

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measured 36 of 36 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-21T16:03:52.814346Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-05-21T16:04:14.623998Z

Reference resolution

35 of 35 outbound references displayed

verified exact3
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2757cc41-b6f0-4503-9ebe-16ba9d8a5a42 · outbound

This paper cites Efficient training for compact compression models via sequential distillation.

Efficient training for compact compression models via sequential distillation Efficient training for compact compression models via sequential distillation

Reference 1

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Observation 4b607579-f778-4e6b-998c-3e306d247ab8 · outbound

This paper cites To support deployment on hardware-constrained devices, we adopt a reduction strategy with lower computa- tional cost in training time and dataset size.

Efficient training for compact compression models via sequential distillation To support deployment on hardware-constrained devices, we adopt a reduction strategy with lower computa- tional cost in training time and dataset size

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T16:04:14.788032Z

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Observation 05d4b147-cca7-42bc-8064-eca1c0bc7a52 · outbound

This paper cites For both architectures, gS s (·) = gT s (·) and EB S (·) = EB T (·), and in the Hyperprior case also hS s (·) = hT s (·).

Efficient training for compact compression models via sequential distillation For both architectures, gS s (·) = gT s (·) and EB S (·) = EB T (·), and in the Hyperprior case also hS s (·) = hT s (·)

Reference 3

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Observation 79ab4985-4860-431e-864b-05d7d5ebc8ab · outbound

This paper cites Under hardware con- straints, storage and training time are challenges.

Efficient training for compact compression models via sequential distillation Under hardware con- straints, storage and training time are challenges

Reference 4

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Observation 5e9b03d0-4c05-42ac-8434-560f78c13f48 · outbound

This paper cites This approach decreases training time, addressing a constraint in resource-limited environments.

Efficient training for compact compression models via sequential distillation This approach decreases training time, addressing a constraint in resource-limited environments

Reference 5

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Observation f60f9f15-ec19-4c1e-abe9-72d37cbc4936 · outbound

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Efficient training for compact compression models via sequential distillation Unresolved cited work

Reference 6

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Observation 7731ae0c-4c40-40ee-8d51-d771f8d7322c · outbound

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Efficient training for compact compression models via sequential distillation Unresolved cited work

Reference 7

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Efficient training for compact compression models via sequential distillation Unresolved cited work

Reference 8

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Observation 0a92bf14-5eaf-4778-b0be-88933592f6b2 · outbound

This paper cites The jpeg still picture compression standard.

Efficient training for compact compression models via sequential distillation The jpeg still picture compression standard

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T16:04:14.785401Z

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Observation 36f54faf-039e-4b16-ab7d-dc64b6dbb3c7 · outbound

This paper cites Intra coding of the hevc standard.

Efficient training for compact compression models via sequential distillation Intra coding of the hevc standard

Reference 10

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Observation c6c46a39-fe74-4bc4-b675-fc6d96364307 · outbound

This paper cites Intra prediction and mode cod- ing in vvc.

Efficient training for compact compression models via sequential distillation Intra prediction and mode cod- ing in vvc

Reference 11

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Observation 1fa48b63-aa50-4c28-ac1f-9add7022491a · outbound

This paper cites An introduc- tion to neural data compression.

Efficient training for compact compression models via sequential distillation An introduc- tion to neural data compression

Reference 12

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Observation dfdb4c3c-7de2-4bbe-97c8-5925436f99cc · outbound

This paper cites End- to-end optimization of nonlinear transform codes for percep- tual quality.

Efficient training for compact compression models via sequential distillation End- to-end optimization of nonlinear transform codes for percep- tual quality

Reference 13

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Observation dcb5c6cb-28b3-4a0f-95a2-8ec25ef80a11 · outbound

This paper cites End- to-end optimized image compression.

Efficient training for compact compression models via sequential distillation End- to-end optimized image compression

Reference 14

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Observation adbaecd7-a112-4d3e-9c80-920e0639b345 · outbound

This paper cites Variational image compression with a scale hyperprior.

Efficient training for compact compression models via sequential distillation Variational image compression with a scale hyperprior

Reference 15

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Observation b33ef6ba-b3a2-4984-9f25-fe4f73639d90 · outbound

This paper cites Computationally-efficient neural image compression with shallow decoders.

Efficient training for compact compression models via sequential distillation Computationally-efficient neural image compression with shallow decoders

Reference 16

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Observation ac5a335b-28de-493a-a2a8-5d4e574a3fec · outbound

This paper cites MCUCoder: Adaptive Bitrate Learned Video Compression for IoT Devices.

Efficient training for compact compression models via sequential distillation MCUCoder: Adaptive Bitrate Learned Video Compression for IoT Devices

Reference 17

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Observation e4e9b353-99de-455a-80c7-067419e9b5ed · outbound

This paper cites Asymmetric autoencoders: An nn alternative for resource- constrained devices in iot networks.

Efficient training for compact compression models via sequential distillation Asymmetric autoencoders: An nn alternative for resource- constrained devices in iot networks

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T16:04:14.739715Z

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Observation 2776b7eb-9247-4457-b229-1be7fab04649 · outbound

This paper cites Block modulating video compression: An ultra low complex- ity image compression encoder for resource limited platforms.

Efficient training for compact compression models via sequential distillation Block modulating video compression: An ultra low complex- ity image compression encoder for resource limited platforms

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T16:04:14.750365Z

Source-reported events for the cited work

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Observation 5f1d7ae2-ad19-4d25-b952-e17f7bd89f13 · outbound

This paper cites Toward edge-based deep learning in industrial in- ternet of things.

Efficient training for compact compression models via sequential distillation Toward edge-based deep learning in industrial in- ternet of things

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T16:04:14.778625Z

Source-reported events for the cited work

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Observation 5b62a75d-d6fc-402c-99e9-b387974924df · outbound

This paper cites The lottery ticket hy- pothesis: Finding sparse, trainable neural networks.

Efficient training for compact compression models via sequential distillation The lottery ticket hy- pothesis: Finding sparse, trainable neural networks

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T16:04:14.754293Z

Source-reported events for the cited work

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Observation 1632d2e0-048f-4df2-9255-c7c848fcadd6 · outbound

This paper cites An improved upper bound on the rate-distortion function of images.

Efficient training for compact compression models via sequential distillation An improved upper bound on the rate-distortion function of images

Reference 22

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Observation 4213f936-4356-41e1-b599-2eccc3f600d1 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Efficient training for compact compression models via sequential distillation Distilling the Knowledge in a Neural Network

Reference 23

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Observation c2240618-261b-41b9-926b-d3b87e242d2f · outbound

This paper cites Sar im- age compression with inherent denoising capability through knowledge distillation.

Efficient training for compact compression models via sequential distillation Sar im- age compression with inherent denoising capability through knowledge distillation

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T16:04:14.764792Z

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Observation 62272624-e589-4719-9b66-0b6584965b14 · outbound

This paper cites Learning-driven lossy image compression: A compre- hensive survey.

Efficient training for compact compression models via sequential distillation Learning-driven lossy image compression: A compre- hensive survey

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T16:04:14.790046Z

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Observation 620c53af-2fc3-4e74-a367-5055b5a1a9ba · outbound

This paper cites Fitnets: Hints for thin deep nets.

Efficient training for compact compression models via sequential distillation Fitnets: Hints for thin deep nets

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T16:04:14.762794Z

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Observation dba12c19-583d-4070-8d7f-64d4597ffe55 · outbound

This paper cites Improving statistical fi- delity for neural image compression with implicit local like- lihood models.

Efficient training for compact compression models via sequential distillation Improving statistical fi- delity for neural image compression with implicit local like- lihood models

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T16:04:14.743787Z

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Observation 659a7454-f586-4a00-b583-d33278e6b462 · outbound

This paper cites High-fidelity generative image compres- sion.

Efficient training for compact compression models via sequential distillation High-fidelity generative image compres- sion

Reference 28

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Source-reported events for the cited work

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Observation 7254636d-b937-4845-adf7-5ca5b33492dc · outbound

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

Efficient training for compact compression models via sequential distillation CompressAI: a PyTorch library and evaluation platform for end-to-end compression research

Reference 29

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Observation 18779d0b-9d9f-42eb-a178-fd92b22ace4a · outbound

This paper cites Neuralcompres- sion.

Efficient training for compact compression models via sequential distillation Neuralcompres- sion

Reference 30

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Observation 2ebe613d-8dd4-4823-8a19-5e48c35c4135 · outbound

This paper cites vimeo 90k 7.

Efficient training for compact compression models via sequential distillation vimeo 90k 7

Reference 31

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Observation 1f88d4e3-9eb4-482a-88aa-811f165e7439 · outbound

This paper cites Kodak lossless true color image suite.

Efficient training for compact compression models via sequential distillation Kodak lossless true color image suite

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T16:04:14.768561Z

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Observation 33aea56c-abf5-4666-af3a-c9acddbd677b · outbound

This paper cites Clic 2020: Challenge on learned image compression.

Efficient training for compact compression models via sequential distillation Clic 2020: Challenge on learned image compression

Reference 33

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Observation 6a53f2cb-f769-47f0-919d-d0c310a7a696 · outbound

This paper cites Video enhancement with task-oriented flow.

Efficient training for compact compression models via sequential distillation Video enhancement with task-oriented flow

Reference 34

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Observation 107022f8-3f89-40ef-b343-848b53061e21 · outbound

This paper cites The open images dataset v4.

Efficient training for compact compression models via sequential distillation The open images dataset v4

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T16:04:14.752295Z

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Pith citing papers

Observation 2757cc41-b6f0-4503-9ebe-16ba9d8a5a42 · inbound