Hierarchical symmetric GAN for Thangka image generation
Bibliographic record
Abstract
Text-to-Thangka generation requires preserving both semantic accuracy and textural details. Current methods struggle with fine-grained feature extraction, multi-level feature integration, and discriminator overfitting due to limited Thangka data. We present HST-GAN, a novel framework combining parallel hybrid attention with differentiable symmetric augmentation. The architecture features a Parallel Spatial-Channel Attention module (PSCA) for precise localization of deity facial features and ritual object textures, along with a Hierarchical Feature Fusion Network (HLFN) for multi-scale alignment. The framework’s Differentiable Symmetric Augmentation (DiffAugment) dynamically adjusts discriminator inputs to prevent overfitting while improving generalization. On the T2IThangka dataset, HST-GAN achieves an Inception Score of 2.08 and reduces Fréchet Inception Distance to 87.91, demonstrating superior performance over baselines on the Oxford-102 benchmark.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".