CuriousAI/mean-teacher

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1678 stars · 342 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Reference implementations (TensorFlow and PyTorch) of the Mean Teacher semi-supervised learning method from the NIPS 2017 paper by Tarvainen and Valpola. It trains a student model alongside a weight-averaged (EMA) teacher model with a consistency cost, achieving state-of-the-art semi-supervised image classification results on CIFAR-10, SVHN, and ImageNet.

Use cases

• train an image classifier with only a small fraction of labeled data
• reproduce the NIPS 2017 Mean Teacher paper results on CIFAR-10 or ImageNet
• apply semi-supervised learning with EMA teacher weights to my own model
• compare semi-supervised methods like temporal ensembling vs mean teacher
• adapt a ResNet to learn from 4000 labels on CIFAR-10
• add a consistency cost between student and teacher outputs in PyTorch

When to choose

• you have limited labeled data for an image classification task
• you want to reproduce or build on the Mean Teacher paper
• you prefer a simple, well-known semi-supervised baseline with PyTorch or TensorFlow code

When to avoid

• you need actively maintained code with recent framework versions
• your task is not image classification or requires heavy customization
• you need the latest state-of-the-art semi-supervised methods (newer techniques exist)

Facets

library · maturity maintenance

machine-learning deep-learning machine-learning deep-learning computer-vision python semi-supervised-learning mean-teacher nips-2017 tensorflow pytorch image-classification research-code gpu

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