Shades of History: Reviving Nepal’s Heritage through AI
Bibliographic record
Abstract
Historical photographs provide vital insights into Nepal’s rich cultural heritage; however, most existing archival collections remain in black and white, limiting visual engagement and cultural comprehension for modern audiences. Despite advancements in AI-driven image colorization, current methods often suffer from inaccuracies in historical and cultural authenticity, highlighting a crucial research gap. This study addresses challenge by employing Conditional Generative Adversarial Networks (cGANs), leveraging a U-Net architecture with a pre-trained ResNet18 backbone. Initially trained using supervised L1 loss and subsequently refined through adversarial training, our method significantly enhances the visual authenticity and accuracy of colorized images. Quantitative assessments yielded a discriminator loss of 0.62 and generator loss of 4.42 for our best model with pretrained backbone. The resulting high-quality colorizations vividly depict historical narratives, greatly enriching the preservation and appreciation of Nepal’s cultural heritage.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".