Neural regularization jointly involving neurons and connections for robust image classification
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents an integrated neural regularization method in fully-connected neural networks that jointly combines the cutting edge of regularization techniques; Dropout [1] and DropConnect [2]. With a small number of data set, trained feed-forward networks tend to show poor prediction performance on test data which has never been introduced while training. In order to reduce the overfitting, regularization methods commonly use only a sparse subset of their inputs. While a fully-connected layer with Dropout takes account of a randomly selected subset of hidden neurons with some probability, a layer with DropConnect only keeps a randomly selected subset of connections between neurons. It has been reported that their performances are dependent on domains. Image classification results show that the integrated method provides more degrees of freedom to achieve robust image recognition in the test phase. The experimental analyses on CIFAR-10 and one-hand gesture dataset show that the method provides the opportunity to improve classification performance.
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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 | 0.001 | 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 it