Semi Supervised Learning in Wild Faces and Videos
Bibliographic record
Abstract
We propose an approach for improving unconstrained face recognition based on leveraging weakly labeled web videos. It is easy to obtain videos that are likely to contain a face of interest from sites such as YouTube through issuing queries with a person’s name; however, many examples of faces not belonging to the person of interest will be present. We propose a new technique capable of learning using weakly or noisly labeled faces obtained in this setting. In particular, we present a novel method for semi-supervised learning using noisy labels which incorporates a margin or null category like property within a fully probabilistic framework. We outline general properties of the approach, showing how the choice of an exponential hyperprior results in an L1 penality which leads to sparse models capable of explicitly accounting for label uncertainty producing state of the art performance. We then illustrate how the margin approach provides robustness and significant performance gains when faces within YouTube search results are combined with the unconstrained face images from the Labeled Faces in the wild dataset.
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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".