Key Factors Determining the Required Number of Training Images in Person Re-Identification
Bibliographic record
Abstract
Focusing on person re-identification datasets, this paper proposes a new method to estimate the test accuracy curve over the training image number in a precise, interpretable, and efficient manner to receive financial and privacy protection benefits. An existing method, neural scaling law, accurately approximates the curve by fitting a regression function to data points of a training image number and the corresponding accuracy. However, fitting such a function does not explain the reason for the estimated curve. Moreover, obtaining a data point updates model parameters with heavy computation. Therefore, this paper investigates the key factors of a person re-identification dataset that determine the regression parameters. By incorporating the found factors, our method becomes interpretable. Simultaneously, the method significantly reduces computation costs since model updates are no longer needed. We experimentally show that our method is as precise as the uninterpretable neural scaling law incurring nearly millions of model updates.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 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".