Writing study: Variance-Based Pruning for Accelerating and Compressing Trained Networks

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← ICCV 2025 oral writing corpus

Uranik Berisha, Jens Mehnert, Alexandru Paul Condurache · Official ICCV 2025 oral record

Observed writing choices, not acceptance rules. This study examines the inspected manuscript version. It does not establish why the paper was accepted or that its presentation choices should be copied unchanged.

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

10 physical pages

Before references / appendix

≈ 8.04 page-equivalents

Main figures / tables

3 / 9

Attached appendix figures / tables

0 / 0

Attached appendix span

No boundary detected; not assumed zero

Before references / appendix: ≈ 80.4%References / intervening material: ≈ 19.6%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 heading	Starts on PDF page	Estimated span	Share of entire PDF
1. Introduction	1	≈ 1.46 pp.	14.6%
2. RELATED WORK	2	≈ 0.73 pp.	7.3%
3. Methodology	3	≈ 1.29 pp.	12.9%
4. Experiments	4	≈ 2.57 pp.	25.7%
5. Analysis	7	≈ 1.36 pp.	13.6%
6. Conclusion	8	≈ 0.27 pp.	2.7%
References	9	≈ 1.96 pp.	19.6%

All detected figure and table locations

Main text

• Figure 1 · p. 2
• Table 1 · p. 5
• Table 2 · p. 5
• Table 3 · p. 5
• Table 4 · p. 6
• Table 5 · p. 6
• Table 6 · p. 7
• Figure 2 · p. 7
• Figure 3 · p. 7
• Table 7 · p. 7
• Table 8 · p. 8
• Table 9 · p. 8

Attached appendix

No recognized caption labels.

Argument and organization

1. 1 Introduction; 2 Related Work

   Separates training, storage and inference costs and narrows pruning to existing trained MLP blocks.

2. 3 Methodology

   Explains online activation statistics, global variance ranking and mean-shift compensation as three numbered steps.

3. 4 Experiments; 5 Ablations; 6 Conclusion

   Separates before/after finetuning results, matched pruning criteria, hybrid token merging and architecture transfer.

Problem, claim and contribution

The motivation is reuse of pretrained networks under limited finetuning, not cheaper initial training from scratch.

Abstract and Sections 1–2, physical pp.1–3 · Main paper

Argument and section order

The three numbered method steps match subsections, moving from the variance criterion to a separate algebraic explanation of why bias absorption shrinks both matrices.

Sections 3.1–3.3, pp.3–4 · Main paper

Figures, captions and visual explanation

The three-panel cartoon distinguishes observed activation distributions, removed neurons replaced by their mean, and the mean transferred into the next bias.

Figure 1, p.2 · Main paper

Tables, comparisons and evidence

The main table distinguishes raw accuracy, retained percentage and post-finetuning accuracy; the caption defines G MACs and M parameters, while the prose warns that MLP pruning rate is not whole-model size reduction.

Table 1 and Section 4.1, p.5 · Main paper

Appendix and reproducibility

Detailed pruning-rate sweeps are routed to Appendix B and pre/post-activation behavior to Appendix E; these main pointers alone were inspected.

Sections 4.1,5.1 and 5.2, pp.5,7–8 · Main paper

Clarity and writing conventions

The setup identifies ten distillation finetuning epochs, optimizer, batch size and H200 GPUs before discussing recovery, keeping immediate retention separate from recovery after additional learning.

Section 4, p.4 · Main paper

What to reuse—and what not to infer

Transferable lesson: Report intervention size, whole-model savings and immediate versus recovered accuracy as separate quantities.

Boundary: Low activation variance is an importance heuristic, not a guarantee across distribution shift. Percentage retention is not percentage-point accuracy change, and the main evidence is ImageNet classification rather than universal task transfer.

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

2, 5

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.

• Official CVF open-access ICCV 2025 accepted-paper PDF, retrieved 7 October 2026; CVF states identical to accepted version except watermark; not assumed identical to initial submission · 10 pages