Multimode Fiber Image Transmission via Cross-Modal Knowledge distillation
Bibliographic record
Abstract
Image transmission through multimode fiber (MMF) has great potential to innovate the systems of endoscopic or neurological imaging. However, when injecting a light pulse with image information into an MMF, the spatial and temporal information is scrambled inside the MMF, leading to chaotic-like outputs of speckles and split pulses. Recently, the development of machine learning has enabled ultrafast MMF image transmission by reconstructing original images from the chaotic output pulses. Despite this, high-cost devices are required for pulse detection at the picosecond-level temporal resolution. Here, we propose a cross-modal knowledge distillation framework to address the device requirement on the customer side. Specifically, by leveraging a teacher model that is well-trained on high-quality data, the student model on the customer side can be trained with lower-quality data while maintaining a good performance.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".