Uzun kuyruklu görsel tanımada sınıf dengesizliğinin öz boyut kullanımı ile azaltılması
Bibliographic record
Abstract
Natural image datasets used in the field of visual recognition are often imbalanced in terms of the number of samples between class categories in the dataset. This problem, defined commonly as class imbalance, results in sub-optimal performance on these under-represented classes for deep learning models which are trained with such datasets. Attempts to remedy this problem include re-sampling, loss re-weighting and other calibration methods which generally use the number of samples as the primary factor in their mitigation strategy, ignoring other factors. In this thesis, we argue that model performance in a dataset depends on the difficulty of individual class categories as well as the number of samples present in the dataset. We use the concept of intrinsic dimensionality to express this idea of difficulty and explore the different definitions and estimation strategies for calculating ID inside a dataset. We further investigate the relationship between ID and class imbalance. Lastly, we report our results on using class ID estimation for class imbalance mitigation on long-tailed variations of natural image datasets -- MNIST-LT, CIFAR-10-LT and CIFAR-100-LT.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.014 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 0.005 |
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; both teacher heads agree on what is shown here.
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".