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Writing study: Cross-Domain Lossy Compression via Rate- and Classification-Constrained Optimal Transport

← ICLR 2026 oral writing corpus

Nam Nguyen, Thinh Nguyen, Bella Bose · Official ICLR 2026 oral record

Figure, table and section ledger

Editorial contribution type: Theory & formal foundations (primary) · Learning & optimization algorithms.

Layout extraction, not a required paper template. Counts are distinct caption labels; a multi-panel figure counts once. Section spans are estimated from heading positions, including floats and whitespace. Method and limitations.

Canonical PDF
50 physical pages
Before references / appendix
≈ 9.17 page-equivalents
Main figures / tables
6 / 0
Attached appendix figures / tables
13 / 15
Attached appendix span
≈ 35.84 page-equivalents
Before references / appendix: ≈ 18.34%References / intervening material: ≈ 9.98%Attached appendix: ≈ 71.68%Estimated layout envelopes, not text/artwork area. Unresolved appendix boundaries remain within the undivided post-body material.
Section-by-section lengths and source pages

The title/abstract/front matter precede the first numbered heading. A span ends at the next detected top-level heading, not at the last sentence; end-page whitespace is included. References and statements are listed separately. These are not measurements of text-only or artwork area.

Top-level headingStarts on PDF pageEstimated spanShare of entire PDF
1 INTRODUCTION1≈ 1.06 pp.2.12%
2 SYSTEM MODEL AND ONE-SHOT SETTING RESULTS2≈ 2.59 pp.5.18%
3 SYSTEM MODEL AND ASYMPTOTIC SETTING RESULTS5≈ 2.5 pp.5%
4 RELATED WORKS7≈ 0.61 pp.1.22%
5 EXPERIMENTAL RESULTS8≈ 1.36 pp.2.72%
6 CONCLUSION9≈ 0.48 pp.0.96%
REFERENCES10≈ 4.98 pp.9.96%
A THEORETICAL RESULTS15≈ 15.95 pp.31.9%
B EXPERIMENTAL RESULTS31≈ 15 pp.30%
C ADDITIONAL EXPERIMENTAL RESULTS46≈ 4.89 pp.9.78%
All detected figure and table locations

Argument and organization

  1. Constrained transport and coding

    Defines separate marginal, rate and label-information requirements.

  2. One-shot and asymptotic regimes

    Organizes Bernoulli and Gaussian claims under distinct coding assumptions.

  3. Neural implementation and empirical trade-offs

    Maps theoretical quantities to practical surrogate losses.

Problem, claim and contribution

The motivation distinguishes restoring a target distribution from reproducing a degraded input. However, the distortion remains between input and reconstruction, so the reader must keep distributional restoration separate from paired clean-target error.

§§1–2, pp.1–3; §5.1, p.8 · Main paper

Argument and section order

Numbered definitions separate one-shot entropy constraints from asymptotic mutual information. The practical rate upper bound and later entropy-model estimate are different quantities, and should retain their units instead of sharing an unexplained rate axis.

Definitions2–3, pp.3,5; §5.1, p.8; AppendixB.1, p.31 · Main paper

Figures, captions and visual explanation

Figure5 links the dither add/subtract path, discriminator, classifier and distortion loss explicitly. Figure18 then places classification accuracy beside conditional-entropy estimates, although labeling a learned-posterior estimate exact overstates what the experiment establishes.

Figure5, p.8; AppendixC.4 and Figure18, p.48 · Main paper

Tables, comparisons and evidence

Appendix tables expose non-monotonic experimental points hidden by general monotonic wording. The Kodak comparison also acknowledges that BM3D has no rate penalty, but supplies no bitrate column, preventing verification of a matched-rate superiority claim.

Tables1–5, pp.41–42; Tables11–12, p.45; Table15, p.50 · Main paper

Appendix and reproducibility

Reading the proofs reveals substantive issues requiring clarification: AppendixA.1.2 replaces H(S|Y) by an average conditional on the shared seed, and AppendixA.1.3 assigns finite mutual information to Y=X for a nondegenerate continuous Gaussian.

AppendixA.1.2, pp.16–17; AppendixA.1.3 Case4, p.21 · Main paper

Clarity and writing conventions

Synthetic samples drawn from a theorem-prescribed coupling check that construction's behavior, not optimality over all couplings. The conclusion's confirmed-theory wording is therefore too strong, especially alongside the unresolved entropy and feasibility issues.

§6, p.9; AppendixC.3, pp.47–48 · Main paper

What to reuse—and what not to infer

Transferable lesson: For theory-to-system papers, preserve the exact conditioning variables, feasible regimes and operational rate units all the way from definitions to figures; empirical agreement is not a substitute for a valid derivation.

Boundary: Full reading is not proof certification. Several supplied formulas and architecture descriptions require correction or reconciliation before scientific reuse. Fluorescence nuclei experiments are not WSI pathology validation; unsupervised restoration still uses semantic labels and target-distribution information in the described objectives.

Apply this lesson using the argument and evidence worksheet.

Inspection record and sources

Checked
2026-09-19
Reading status
Full text + selected visuals inspected
Main text
pp.1–9, §§1–6; references pp.10–14
Appendix / supplement
pp.15–50, A–C: complete supplied proofs, rate estimation, restoration protocols, architecture tables, synthetic checks and additional comparisons
Visually inspected PDF pages
8, 48