Using the Supervised Descent Method to Improve the Initial Guess of Optimization-Based Microwave Imaging Methods
Bibliographic record
Abstract
Machine learning techniques for microwave imaging have seen significant recent progress (N. Khalid et al., npj Imaging, 2, 1, 2024). However, optimization-based algorithms (e.g. Contrast Source Inversion (P. M. van den Berg and A. Abubakar, Prog. Electromagn. Res., 34, 189-218, 2001)) still have the benefit of generalizability without a large training dataset, as well as using the underlying physics of the problem. One of the costs of using ML imaging methods is that they are only as good as the training data set and how well it covers the target space. In order to have a generalizable ML algorithm, thousands of training examples are usually required (even in 2D imaging). One of the issues with optimization imaging methods is the dependence on the quality of the initial guess (in our experience, this is especially true with 3D imaging with limited data).
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".