Methods › General › Semi-Supervised Learning Methods

Semi-Supervised Learning Methods

23 methods 469 papers tagged archive 2025-07-28

The archive attaches this collection's text per method and the copies differ: 2 distinct texts across 23 of the 23 methods here. All are shown, most-carried first (a tie goes to the text carrying Papers with Code's collection boilerplate, then to the longer text); no vote is taken between them.

Text 1, carried by 20 of 23 methods:

Semi-Supervised Learning methods leverage unlabelled data as well as labelled data to increase performance on machine learning tasks. Below you can find a continuously updating list of semi-supervised learning methods (this may have overlap with self-supervised methods due to evaluation protocol similarity).

Text 2, carried by 3 of 23 methods:

Self-Supervised Learning refers to a category of methods where we learn representations in a self-supervised way (i.e without labels). These methods generally involve a pretext task that is solved to learn a good representation and a loss function to learn with. Below you can find a continuously updating list of self-supervised methods.

Methods

All 23 methods in this collection, most-tagged first. Year is the archive's introduced_year; the archive stores 2000 when it has none, shown here as “–”. Papers counts distinct papers the archive tags with the method. Click a heading to sort.

Contrastive Predictive Coding – 113
FixMatch – 85
SPS Semi-Pseudo-Label – 47
SPL Semi-Pseudo-Label – 45
Noisy Student – 38
Transductive Inference – 33
MoCo v2 – 30
IPL Iterative Pseudo-Labeling – 17
LPM Local Prior Matching – 17
Gradual Self-Training – 8
STAC – 8
Pattern-Exploiting Training – 6
DifferNet – 5
Meta Pseudo Labels – 5
SimCLRv2 – 4
MixText – 3
M3L Multi-modal Teacher for Masked Modality Learning – 2
CPC v2 – 1
MAD Learning Memory-Associated Differential Learning – 1
PinvGCN Pseudoinverse Graph Convolutional Network – 1
SKEP – 1
STraTA Self-Training with Task Augmentation – 1
State-Aware Tracker – 1