PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
August 22, 20260 citationsOpen Access

Technical Report v1.14: Four-Model Verification of the Failure Fingerprint Statistics via an Independent, Join-Free Database — Errata and Supplement to Technical Report v1.10 and v1.12

View Full Paper
NMneco mohumohu

Key Points

  • Correct and verify failure fingerprint statistics across four image reconstruction models using an independent, join-free verification database.
  • Re-derived mu_stat and sigma_stat values across four models (Upscayl Standard, SwinIR, Digital-Art, Upscayl High-Fidelity) using an identical exact-methodology-matched, join-free pipeline on the NIH ChestX-ray8 dataset.
  • Evaluated the SSIM=1.0 Pixel Identity transitivity corollary across model population sizes ranging from N=15 to N=2,657,100 blocks.
  • Investigated potential cross-model numerical artifacts using boundary-condition analysis of the split algorithm.
  • Upscayl High-Fidelity was severely affected by database joining bugs, with mu_stat corrected from 0.1400 to 0.0100 and sigma_stat from 0.0800 to 0.0860, while Upscayl Standard sigma_stat was corrected from 0.0800 to 0.101.
  • SwinIR and Digital-Art were confirmed completely unaffected on both metric axes, despite a 54.1% reduction in underlying population count for Digital-Art.
  • The SSIM=1.0 Pixel Identity transitivity corollary was confirmed with zero exceptions across all four models.

Abstract

Errata and supplement to Technical Report v1. 10 and v1. 12. Technical Report v1. 12 corrected the Failure Fingerprint statistics (muₛtat, sigmaₛtat, and their deviation coordinates) for Upscayl Standard and SwinIR against an independent, join-free verification database, and explicitly deferred Upscayl High-Fidelity and Digital-Art to a later report. This report completes that remaining work for all four models studied in this series, re-deriving every value under the identical exact-methodology-matched procedure. Result: Upscayl Standard's sigmaₛtat is corrected from 0. 0800 to 0. 101 (confirming Technical Report v1. 12) ; SwinIR is confirmed fully unaffected on both axes; Digital-Art is confirmed fully unaffected on both axes despite a 54. 1% reduction in its underlying population count; Upscayl High-Fidelity is severely affected on both axes -- muₛtat corrected from 0. 1400 to 0. 0100, with the deviation coordinate collapsing from roughly 19x the theoretical constant to essentially on it, and sigmaₛtat corrected from 0. 0800 to 0. 0860. No two models show the identical impact pattern, confirming the bug's consequences are model-specific rather than a single fixable constant. As a secondary, independent cross-check, the SSIM=1. 0 Pixel Identity transitivity corollary (every comp_* feature matching exactly between GT, noTTA, and TTA on blocks where both reconstructions score SSIM=1. 0) is confirmed with zero exceptions across all four models, at population sizes ranging from 15 (High-Fidelity) to 2, 657, 100 (SwinIR). Along the way, a numerical coincidence between High-Fidelity's and SwinIR's corrected muₛtat values is investigated directly and resolved as a boundary-condition artifact of the split algorithm's own lower bound, not a genuine cross-model connection -- documented explicitly to prevent later mis-citation. Together, these results are framed as demonstrating two things at once: that the original join-based database construction carries real, model-specific risk rather than a single isolated defect, and that the new join-free pipeline is a validated correction, not merely a theoretical one -- alongside a reusable five-step procedure for trusting, or correctly distrusting, any aggregate statistic built on a join-based or estimator-based pipeline. Upscayl Standard's and SwinIR's databases are identical to those already independently verified and evidenced in Technical Report v1. 12 and v1. 12. 1; this report cites that existing evidence directly and provides fresh hashes and screen-recorded video evidence only for Upscayl High-Fidelity and Digital-Art, new to this report. Dataset and Mandatory Citation: - Source: NIH ChestX-ray8 (Hospital-scale chest x-ray database) - Citation: Wang, X. , Peng, Y. , Lu, L. , Lu, Z. , Bagheri, M. , & Summers, R. M. (2017). "ChestX-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thoracic diseases. " Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 3462–3471. - Download: https: //nihcc. app. box. com/v/ChestXray-NIHCC Contact: s. shiny. n. works@gmail. com

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

neco mohumohu (2026) studied this question.

synapsesocial.com/papers/6a895f2aca7ade938187da61https://doi.org/10.5281/zenodo.22012409
Ask AI
Helpful
Bookmark
Share
View Full Paper