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Writing study: Knowledge Distillation for Learned Image Compression

← ICCV 2025 oral writing corpus

Yunuo Chen, Zezheng Lyu, Bing He, Ning Cao, Gang Chen, Guo Lu, Wenjun Zhang · Official ICCV 2025 oral record

Figure, table and section ledger

Editorial contribution type: Efficiency, inference & systems (primary).

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
11 physical pages
Before references / appendix
≈ 8.05 page-equivalents
Main figures / tables
11 / 4
Attached appendix figures / tables
0 / 0
Attached appendix span
No boundary detected; not assumed zero
Before references / appendix: ≈ 73.18%References / intervening material: ≈ 26.82%Estimated layout envelopes, not text/artwork area. Unresolved appendix boundaries remain within the undivided post-body material.

Extraction notes: No attached appendix boundary detected. Zero means no recognized caption, not independently verified absence.

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≈ 0.81 pp.7.36%
2. Related Work2≈ 0.63 pp.5.73%
3. Method3≈ 3.22 pp.29.27%
4. Experiments6≈ 2.19 pp.19.91%
5. Conclusion8≈ 0.31 pp.2.82%
References9≈ 2.95 pp.26.82%
All detected figure and table locations

Attached appendix

    No recognized caption labels.

    Argument and organization

    1. 1 Introduction; 2 Related Work

      Separates rate-distortion quality from model complexity and identifies architecture mismatch and supervision as distillation barriers.

    2. 3 Method

      Defines LIC components, modular transform/entropy distillation, teacher-shaped student construction and implicit end-to-end supervision.

    3. 4 Experiments; 5 Conclusion

      Separates the resulting codec, distillation-versus-vanilla controls, architecture alignment and extensions to two other teachers.

    Problem, claim and contribution

    Distinguishes the general SMoDi training framework from KDIC, a particular compressed S2CFormer-derived student.

    Abstract and contribution list, physical pp.1–2 · Main paper

    Argument and section order

    The rate-distortion objective precedes modular decomposition; the channel-energy diagnosis then motivates why plain intermediate-feature matching is insufficient.

    Sections 3.1–3.4 and Figure 4, pp.3–6 · Main paper

    Figures, captions and visual explanation

    The overview labels six teacher/student stages, tensor resolutions and channel/block counts, with frozen/trainable symbols exposing the active module and inherited context.

    Figure 2, p.4 · Main paper

    Tables, comparisons and evidence

    Table 2 includes teacher, vanilla student and distilled student, separating architecture savings from training gains; Table 1 states dataset-specific BD-rate alongside parameter, FLOP, latency and training-throughput measures.

    Tables 1–2, p.7; timing protocol, pp.6–7 · Main paper

    Appendix and reproducibility

    The preliminary Jacobian/KL rationale explicitly defers detailed proof and discussion to the appendix; only the main argument and pointer were scanned, not proof-verified.

    Section 3.2.1, p.4 · Main paper

    Clarity and writing conventions

    The extension section calls its two added student designs examples rather than an exhaustive search, appropriately narrowing the generalizability claim.

    Section 4.5, p.8 · Main paper

    What to reuse—and what not to infer

    Transferable lesson: Always retain teacher, un-distilled student and distilled student rows so architecture compression and supervision benefits cannot be confused.

    Boundary: Figure 1 describes a 2.5% BD-rate reduction versus the teacher, but Table 2 attributes the 2.50-point improvement to the vanilla student; the teacher remains better in BD-rate. The complexity and throughput protocols use different image sizes and should not be combined into one speed claim.

    Apply this lesson using the argument and evidence worksheet.

    Inspection record and sources

    Checked
    2026-10-07
    Reading status
    Writing-focused manuscript scan + selected visuals inspected
    Main text
    Writing-oriented scan across the main manuscript, physical PDF pp.1–8, covering framing, section progression, method explanations, captions, comparison protocols and concluding scope; reference boundary checked. Selected full-page rasters were inspected. Not exhaustive line-by-line reading, proof verification or experimental reproduction.
    Appendix / supplement
    Main-paper appendix/supplement references were checked. A separate supplementary source is linked in the inventory but was not retrieved or inspected; its contents are not included in the reading or visual-page counts.
    Visually inspected PDF pages
    4, 7, 8

    This is a writing-focused scan of the manuscript and the stated supplementary scope: framing, organization, visual explanation, evidence presentation and wording. It is not a line-by-line proof audit, reproduction of results or exhaustive scientific review.