Semi-Supervised Multi-Label Learning from Crowds via Deep Sequential Generative Model
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
Multi-label classification (MLC) is pervasive in real-world applications. Conventional MLC algorithms assume that enough ground truth labels are available for training a classifier. While in reality, obtaining ground truth labels is expensive and time-consuming. In the field of data mining, it is more efficient to use crowdsourcing for label collection. In this setting, an MLC algorithm needs to deal with the noisiness of the crowdsourced labels as well as the remaining massive unlabeled data. In this paper, we propose a deep generative model to describe the label generation process for this semi-supervised multi-label learning problem. Although deep generative models are widely used for MLC problems, no previous work could address the noisy crowdsourced multi-labels and unlabeled data simultaneously. To address this challenging problem, our novel generative model incorporates latent variables to describe the labeled/unlabeled data as well as the labeling process of crowdsourcing. We introduce an efficient sequential inference model to approximate the model posterior and infer the ground truth labels. Our experimental results on various scales of datasets demonstrate the effectiveness of our proposed model. It performs favorably against four state-of-the-art deep generative models.
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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 | 0.001 |
| 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