Deep-Learning Supervised Snapshot Compressive Imaging Enabled by an End-to-End Adaptive Neural Network
Bibliographic record
Abstract
Snapshot compressive imaging (SCI) is an advanced approach for single-shot high-dimensional data visualization. Deep learning is popularly used to improve SCI's performance. However, most existing methods are merely used as a replacement for analytical-modeling-based image reconstruction. Moreover, these models cling to the conventional random coded apertures and often presume a linear shearing operation. To overcome these limitations, we develop a new end-to-end convolutional neural network, termed deep high-dimensional adaptive net (D-HAN) that offers multi-faceted supervision to SCI by optimizing the coded aperture, sensing the shearing operation, and reconstructing three-dimensional datacubes. The D-HAN is implemented in two representative SCI systems for ultrahigh-speed imaging and hyperspectral imaging. The D-HAN is envisioned to benefit SCI in system design, image reconstruction, and performance evaluation.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".