MGHCNET: A Deep Multi-Scale Granular and Holistic Channel Feature Generation Network for Image Super Resolution
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
Residual blocks use skip connections in order to facilitate the flow of information in the network and thus, provide a good network performance. As different objects in a generic image appear at different scales, employing a multi-scale feature generation module in a residual block for image super resolution can further improve the network performance. In this paper, a new residual block that generates features at multiple scales is proposed for the task of image super resolution. In order to enhance the representational capability of the network while keeping its complexity low, the proposed residual block uses two different feature generation techniques, namely, multi-scale granular channel feature generation and uni-scale holistic channel feature generation, and fuses their output feature maps. It is shown that the network using the proposed residual block outperforms the state-of-the-art lightweight super resolution networks on four benchmark datasets with various scaling factors.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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