Stage-wise Training: An Improved Feature Learning Strategy for Deep Models
Why this work is in the frame
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Bibliographic record
Abstract
Deep neural networks currently stand at the state of the art for many machine learning applications, yet there still remain limitations in the training of such networks because of their very high parameter dimensionality. In this paper we show that network training performance can be improved using a stage-wise learning strategy, in which the learning process is broken down into a number of related sub-tasks that are completed stage-bystage. The idea is to inject the information to the network gradually so that in the early stages of training the \coarse-scale properties of the data are captured while the \nerscale characteristics are learned in later stages. Moreover, the solution found in each stage serves as a prior to the next stage, which produces a regularization eect and enhances the generalization of the learned representations. We show that decoupling the classier layer from the feature extraction layers of the network is necessary, as it alleviates the diusion of gradient and over-tting problems. Experimental results in the context of image classication support these claims.
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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 | 0.002 | 0.013 |
| 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