PHMNet: A Deep Super Resolution Network using Parallel and Hierarchical Multi-Scale Residual Blocks
Bibliographic record
Abstract
Deep image super resolution networks use a nonlinear end-to-end mapping between the low and high resolution versions of an image and therefore, provide a good performance. As the different parts of a single image appear in different scales, developing a deep learning based image super resolution scheme that is capable of generating features at different scales and levels is essential. In this paper, a new residual block is proposed with a view of generating a rich set of features extracted at different scales and levels. The development of the proposed block is carried out using two distinct strategies, the first one focussing on generating features directly in two different scales, whereas the second one aims at generating multi-scale features indirectly by extracting them from two different hierarchical levels of abstraction. It is shown through experimental results that the proposed scheme of designing the residual block results in a network that provides a superior performance with reduced number of parameters than that provided by the light-weight networks using other types of residual blocks.
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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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".