Methods › General › Semi-Supervised Learning Methods › SPS

Semi-Pseudo-Label

SPS

47 papers tagged archive 2025-07-28

Introduced by Tamas Matuszka et al. in A Novel Neural Network Training Method for Autonomous Driving Using Semi-Pseudo-Labels and 3D Data Augmentations

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

The archive carries no description for this method.

PaperSource

Papers archive 2025-07-28

30 shown of 47, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

20 shown of 48 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Scheduling6
Diagnostic4
regression4
Language Modelling3
Data Augmentation2
Language Modeling2
Reinforcement Learning (RL)2
Sentence2
Abstractive Text Summarization1
Autonomous Driving1
CPU1
Clustering1
Computational Efficiency1
Deep Reinforcement Learning1
Depression Detection1
Direction of Arrival Estimation1
EEG1
FAD1
Fairness1
Feature Engineering1

Usage over time archive 2025-07-28

Papers per year tagged with SPS: 2022 to 2025, peak 23 23 0 2022: 2 papers 2022 2023: 18 papers 2023 2024: 23 papers 2024 2025: 4 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (47 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Semi-Supervised Learning Methods

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