Fast CNN enhancement using channel attention and residual networks 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
Single image super-resolution (SISR) refers to the process of reconstructing a high-resolution (HR) image from a low-resolution (LR) input image. Deep learning super-resolution algorithms have widely been used to solve SISR tasks. However, the demanding computation cost and memory occupation incurred through training the deep learning models has been hindering its real-world application. In this paper, we rebuild FSRCNN and apply it to solve SISR tasks. Firstly, we change the original training dataset to RealSR, a larger dataset consisting of real-world images. Secondly, channel attention and residual blocks have been applied to the mapping layers and important parameters including learning rate and optimizer have been reset. Thirdly, we change the cost function from loss to loss and replace the activation function from parametric rectified linear unit (PReLU) to exponential linear unit (ELU), to verify the discrepancies between different loss functions and activation functions. Finally, we compare the rebuilt models with the official FSRCNN based on the Peak signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM) on three common test datasets. The original model achieves better performance on all the test datasets across different scale factors while the rebuilt models show better generalization capability. Our analyses illustrate that residual blocks can slightly promote model performance while different loss functions and activation functions do not generate an evident impact on the rebuilt model.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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