Papers › Self Meta Pseudo Labels: Meta Pseudo Labels Without The Teacher
Self Meta Pseudo Labels: Meta Pseudo Labels Without The Teacher
Kei-Sing Ng, Qingchen Wang
We present Self Meta Pseudo Labels, a novel semi-supervised learning method similar to Meta Pseudo Labels but without the teacher model. We introduce a novel way to use a single model for both generating pseudo labels and classification, allowing us to store only one model in memory instead of two. Our method attains similar performance to the Meta Pseudo Labels method while drastically reducing memory usage.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Semi-Supervised Image Classification | CIFAR-10, 4000 Labels | Self Meta Pseudo Labels | Percentage error | 4.09 | #10 of 49 | Archive leaderboard | report |
| Semi-Supervised Image Classification | cifar-100, 10000 Labels | SMPL (WRN-28-8) | Percentage error | 21.68 | #9 of 29 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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