Showcasing Mixture of Experts on CIFAR-10

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…