Shades of History: Reviving Nepal’s Heritage through AI
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it