Lightweight Transformer for Image Interpolation Via Unrolling of Multiple Learned Graph Laplacian Regularizers
Bibliographic record
Abstract
We build an interpretable and lightweight transformer-like neural net by unrolling an iterative algorithm that minimizes multiple realizations of the quadratic graph Laplacian regularizer (GLR), subject to an interpolation constraint. The pivotal insight is that a normalized signal-dependent graph learning module amounts to a variation of the self-attention mechanism in conventional transformers. Unlike “blackbox” transformers that require learning of large key, query and value matrices to compute transformed dot products as affinities and output embeddings, we employ shallow CNNs to learn low-dimensional features per pixel to establish pairwise Mahalanobis distances and construct sparse similarity graphs. At each layer, given a learned graph, the target interpolated signal is simply a low-pass filtered output derived from the minimization of GLRs, resulting in a steep reduction in parameter count. Image interpolation experiments demonstrate competitive restoration performance and notable parameter reduction compared to mainstream transformers.
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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 | 0.001 | 0.002 |
| 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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".