Writing study: Orthogonal Concept Erasure for Diffusion Models
← ICML 2026 oral writing corpus
Yuhao Sun, Lingyun Yu, Hao-Xiang Xu, Fengyuan Miao, Zhuoer Xu, Hongtao Xie · Official ICML 2026 oral record
Figure, table and section ledger
Editorial contribution type: Training, objectives & optimization (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
- 22 physical pages
- Before references / appendix
- ≈ 9.21 page-equivalents
- Main figures / tables
- 4 / 6
- Attached appendix figures / tables
- 4 / 7
- Attached appendix span
- ≈ 9.91 page-equivalents
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 | ≈ 0.99 pp. | 4.5% |
| 2. Related Work | 2 | ≈ 0.51 pp. | 2.32% |
| 3. Geometric Analysis of Concept Erasure | 3 | ≈ 1.01 pp. | 4.59% |
| 4. Problem Formulation | 4 | ≈ 0.53 pp. | 2.41% |
| 5. Orthogonal Concept Erasure (OCE) | 4 | ≈ 1.5 pp. | 6.82% |
| 6. Experiments | 6 | ≈ 3.34 pp. | 15.18% |
| 7. Limitations. | 9 | ≈ 0.1 pp. | 0.45% |
| 8. Conclusion | 9 | ≈ 0.2 pp. | 0.91% |
| Acknowledgments | 10 | ≈ 0.05 pp. | 0.23% |
| Impact Statement | 10 | ≈ 0.12 pp. | 0.55% |
| References | 10 | ≈ 2.88 pp. | 13.09% |
| A. Additional Theoretical Analysis | 13 | ≈ 1.32 pp. | 6% |
| B. Extented Related Works | 14 | ≈ 0.28 pp. | 1.27% |
| C. Implementation Details | 14 | ≈ 1.75 pp. | 7.95% |
| D. Additional Experiments | 16 | ≈ 6.57 pp. | 29.86% |
All detected figure and table locations
Main text
Argument and organization
- 1 Introduction; 2 Related Work; 3 Geometric Analysis
Uses controlled transformations to motivate preserving angular geometry during editing.
- 4 Problem Formulation; 5 Orthogonal Concept Erasure
Moves from additive fitting to a closed-form orthogonal objective, then relaxes erasure to the subspace level.
- 6 Experiments; 7 Limitations; 8 Conclusion
Tests target removal, preservation, scale, adversarial variants and architecture transfer.
Problem, claim and contribution
The core tradeoff is erasure versus retained generation quality, and the proposed restriction is motivated by a three-case geometric diagnostic rather than only leaderboard performance.
Section 3 and Figure 2, p.3 · Main paper
Argument and section order
The simpler vector-wise objective precedes the multi-concept subspace objective, with the preservation term deliberately kept vector-wise.
Sections 4-5, pp.4-6 · Main paper
Figures, captions and visual explanation
The toy panel connects three geometry operations to generated outputs; the multi-concept page aligns target/non-target examples with a table separating erasure, preservation, aggregate score and runtime.
Figure 2, p.3; Figure 3 and Table 3, p.7 · Main paper
Tables, comparisons and evidence
Target recognition rates, non-target recognition, COCO quality and runtime are reported separately; adversarial editing is explicitly marked as an added variant.
Sections 6.1-6.3 and Tables 1-4, pp.6-9 · Main paper
Appendix and reproducibility
Derivations and full target/retain sets make the closed-form objective and evaluation categories inspectable rather than leaving concept selection implicit.
Appendix A, p.13; Table 13, p.18 · Main paper
Clarity and writing conventions
The limitations acknowledge intermediate semantics instead of exact anchor matching and larger-model SVD overhead, which qualifies the language of precise erasure.
Section 7, p.9 · Main paper
What to reuse—and what not to infer
Transferable lesson: Use diagnostic interventions to motivate a constrained edit, then report removal and preservation on distinct axes and name every robustness extension.
Boundary: Preserving weight geometry is not a proof of semantic preservation or universal erasure; detector-based metrics and a few attacks define the tested scope. Runtime should distinguish editing from reusable offline preservation-prior computation.
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 of the main argument through physical p.9, with focused physical-page text inspection on 1, 3, 4, 5, 6, 7, 8, 9. Framing, section progression, mechanism/proof explanation, displays, comparison conditions and conclusions were inspected. This is not exhaustive technical reading, proof verification or reproduction.
- Appendix / supplement
- Appendix organization and main-text pointers were checked; focused text inspection of physical pages 13, 15, 16, 18. Remaining appendix proofs and details were not exhaustively read. No separately linked supplement inspected.
- Visually inspected PDF pages
- 3, 7
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.