Showcasing Mixture of Experts on CIFAR-10

Abdulkader Helwan
6 min readJan 7, 2024

We all recently heard that ChatGPT and GPT-3 were made based on an approach called Mixture of Experts (MoE). Such an approach has gained traction is the machine learning field which is a powerful paradigm that excels in handling complex, high-dimensional data. In this blog post, we embark on an enlightening step-by-step tutorial to develop, train, test, and validate a Mixture of Experts for the classification of images from the CIFAR-10 dataset.

To implement MoE for image classification, we leverage the CIFAR-10 dataset, a benchmark in computer vision. With 60,000 32x32 color images across 10 classes, CIFAR-10 is a challenging playground to showcase the capabilities of MoE.

CIFAR-10 Classification Using Mixture of Experts. Drawn by Author

By the end of this story, you will understand the basics of a Mixture of Experts, and how to develop a MoE for basic and simple classification problems.

I already published some stories related to Mixture of Experts and its applications. Find them here and here and here.

P.S. This is not a very theoretical article. it is rather a How-To article on getting started with MoE for image classification.

Understanding Mixture of Experts:

Mixture of Experts is a neural network architecture that divides the learning task into multiple sub-tasks, assigning each to a specialized expert. These experts operate independently and contribute to the final prediction through a gating mechanism. This allows the model to adaptively choose which expert or combination of experts to rely on, enhancing its ability to handle diverse and intricate datasets.

Model Architecture and Design:

Our MoE architecture comprises of 3 expert networks responsible for handling specific features of the images. These experts work in parallel with a gating network, which learns to assign weights to each expert based on the input data. The collective decision made by the experts through the gating mechanism yields the final classification output.

One important aspect of building such a method is to select the right expert models artchitecture and select a good gating model too, Usually, this depends on the task itself. in this article, our experts will be simple convolutional neural…

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