Deep Learned Cumulative Attribute Regression
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
Learning regression-based machine learning models for computer vision problems is a challenging task due to noisy features, variation in pose and illumination, occlusion, etc. Typically the problem is compounded by the non-uniform distribution of labels in the training data, resulting in parts of the label space that suffer from data sparsity and a problem of label imbalance in general. Deep Convolutional Neural Networks (CNN) have shown remarkable success on a number of computer vision tasks such as object classification and face recognition. However, they too suffer from sparse and imbalanced training datasets for regression problems, even when those datasets are very large. Cumulative Attributes have previously been proposed to address the issue of label imbalance, but to date this concept has not been integrated with Deep Learning. In this work, we propose a CNN-based framework for learning regression models by using Cumulative Attributes as intermediate features. We evaluate our method on a number of tasks which includes pain intensity estimation, Facial Action Unit intensity estimation and age estimation. Our results show that the proposed method is robust to imbalance and sparsity present in the training datasets, and performs significantly better than the current methods where CNNs are learnt directly for regression.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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