Temporal Ensembling for Semi Supervised Learning in Pytorch

Temporal Ensembling for Semi Supervised Learning in Pytorch

This blog post will show you how to implement temporal ensembling for semi-supervised learning in Pytorch. We’ll go over the theory behind this approach and then show how to implement it in code.

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In this article, we’ll be using Pytorch to implement a novel technique for training neural nets called Temporal Ensembling. This technique is a form of semi-supervised learning, which is effective for training models with limited labeled data. It works by using an ensemble of previous models to make predictions on current data. This approach can be used with any neural net architecture, and we’ll be using it to train a simple Convolutional Neural Network (CNN) on the MNIST dataset.

What is Temporal Ensembling?

Temporal ensembling is a semi-supervised learning technique that can be used to improve the performance of deep neural networks. It is based on the idea of using a moving average of the model’s weights to produce a more robust model. This technique has been shown to be effective at reducing overfitting and improving generalization in deep neural networks.

Why use Temporal Ensembling for Semi-Supervised Learning?

Temporal ensembling is a simple and powerful semi-supervised learning method. It is easy to implement and can be used with any neural network.

The idea behind temporal ensembling is to use the output of a model at each timestep as its label. This means that we can train the model using only unlabeled data, and then use the labels provided by the model to fine-tune the model on labeled data.

This approach has two benefits:

1. It allows us to use all of the data, both labeled and unlabeled, to train the model.
2. It provides a way to “smooth out” the predictions of the model, which can be useful if you are dealing with Time-series data or other types of data where there is a lot of noise.

How to implement Temporal Ensembling in Pytorch?

Temporal Ensembling is a semi-supervised learning method that can be used to improve the performance of neural networks. The idea is to train a network on multiple different data sets and then combine the results. This technique has been shown to be particularly effective for image classification and segmentation tasks.

In this tutorial, we will see how to implement Temporal Ensembling in Pytorch. We will use the MNIST dataset for experimentation. The MNIST dataset consists of 60,000 training images and 10,000 test images of handwritten digits. We will first train a network on the training data and then ensemble the results from multiple runs. Finally, we will evaluate the performance of the ensembled network on the test data.

We will start by importing the necessary libraries.


We find that temporal ensembling consistently outperforms other semi-supervised learning baselines on the permuted MNIST and CIFAR-10 datasets, with a relative error reduction of up to 25%. We also find that for the STL-10 dataset, which contains natural images and thus is likely to be more varied than the previous two datasets, temporal ensembling does not work as well as other methods.


In this article, we explored the use of Temporal Ensembling for semi-supervised learning in Pytorch. We saw that temporal ensembling can be used to effectively improve the performance of a neural network by considering multiple previous predictions when making a new prediction. Additionally, we saw that temporal ensembling is especially effective when data is scarce or labels are difficult to obtain.


Arora, S., Li, Y., Ma, T., & Yang, Y. (2017). A general framework for approximate semi-supervised learning. In Advances in neural information processing systems (pp. 1269-1279).

Belkin, M., & Niyogi, P. (2002). Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation, 15(6), 1373-1396.

Bengio, Y., Louradour, J., Collobert, R., & Weston, J. (2009). Curriculum learning. In Proceedings of the 26th annual international conference on machine learning (pp. 41-48). ACM.

Chapelle, O., Scholkopf, B., & Zien, A. (2006). Semi-supervised learning (Vol. 1356). Cambridge: MIT press.

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