Search, Estimate, and Predict: Efficient Weakly-Supervised Learning
Bibliographic record
Abstract
Most of state-of-the-arts region-based detectors are suffered from the requirement of training for a large volume of bounding box annotated data. In this paper, we propose an innovative weakly-supervised architecture, namely SEP-Net, that employs an efficient proposal searching method on feature maps to generate region proposals via transforming CNN classifiers to object detectors without bounding box annotations. We further show the proposed framework can be used for real-time detection, in which high computation efficiency is a prerequisite. Moreover, the training procedure is exactly the same as what’s for CNN classifiers, so is easy to transfer a pre-trained CNN to our weakly-supervised detector as well. Experimental validation on ImageNet 2012 dataset show that the novel model outperforms classical and most recent state-of-the-art weakly-supervised method by a wide margin in terms of both accuracy and efficiency.
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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 | 0.000 |
| 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) | 0.000 | 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".