Light Siamese Neural Network Architecture for Image Comparison
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
Deep neural networks typically require large training sets and powerful processing hardware, thus making their use difficult in training data or processing resource-limited systems. Siamese neural networks use few-shot learning to address this problem. However, their twins are typically implemented with standard multilayered perceptrons (MLP) or convolutional neural networks (CNN), which still require large training sets and powerful processing hardware. We propose a simplified Siamese model that uses a single Inception module for the twins instead of a full Inception CNN, for 20 times less parameters to learn and an easier implementation. Our validation experiments to assess whether the image of a molecular structure bears similarity with a reference shows 95.6% average accuracy for three different reference molecules, using training sets of just 25 randomly selected pairs of positive and negative examples for the twins. The training took 43 minutes to complete on an Intel I5-based PC station with no GPU acceleration.
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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 | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it