Recurrent Network Knowledge Distillation for Image Rain Removal
Bibliographic record
Abstract
Single-image rain removal (SIRR) based on deep learning has long been a problem of great interest in low-level vision systems. However, traditional convolutional neural network (CNN)-based approaches fail to capture long-range location dependencies effectively and may cause the image background blurred. In this article, we propose a knowledge distilling deraining network (KDRN) to address the SIRR problem. In the proposed network, the teacher regards rain streaks as a linear combination of many residual networks. It is used for image reconstruction at different resolutions. With the aid of a teacher network, the proposed deraining network performs better. A spatial channel aggregation residual attention block (SCARAB) is designed to remove the rain streaks. The block not only concentrates on the rain streak features but also captures the spatial-channel information of the image. For the network structure, we used an end-to-end approach to design the teacher and student networks separately. The proposed KDRN obtains the predicted residual image by a combination of the stage-wise results and the original input image. Extensive experiments show that the proposed KDRN obtains better subjective quality than most of the compared methods, on both heavy and light rain data sets.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.002 | 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".