Srnmfrb: A Deep Light-Weight Super Resolution Network Using Multi-Receptive Field Feature Generation Residual Blocks
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Bibliographic record
Abstract
Deep neural networks use a nonlinear end-to-end mapping in order to transform a low resolution image to the high resolution one. Residual blocks facilitate the flow of the information in deep neural networks and enhance the network performance. In this paper, a new residual block that enhances the representational capability of a super resolution network is proposed. The proposed residual block combines the features generated in various receptive fields using different hierarchical levels of convolution operations or convolution operations in conjunction with the space-to-depth and depth-to-space operations in order to provide a rich set of residual features. The experimental results demonstrate the superiority of the super resolution network using the proposed residual block over the state-of-the-art light-weight super resolution networks in terms of objective and subjective metrics.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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