Applying Error-Correcting Output Coding to Enhance Convolutional Neural Network for Target Detection and Pattern Recognition
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Bibliographic record
Abstract
This paper views target detection and pattern recognition as a kind of communications problem and applies error-correcting coding to the outputs of a convolutional neural network to improve the accuracy and reliability of detection and recognition of targets. The outputs of the convolutional neural network are designed according to codewords with maximum Hamming distances. The effects of the codewords on the performance of the convolutional neural network in target detection and recognition are then investigated. Images of hand-written digits and printed English letters and symbols are used in the experiments. Results show that error-correcting output coding provides the neural network with more reliable decision rules and enables it to perform more accurate and reliable detection and recognition of targets. Moreover, our error-correcting output coding can reduce the number of neurons required, which is highly desirable in efficient implementations.
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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 | 0.001 | 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