When it comes to classification tasks, multi-head classifiers are often considered the go-to solution. But what if I told you that they’re not always the best choice? In fact, adaptive classifiers can be a game-changer in real-world systems where new categories emerge regularly.
I recently worked on building enterprise classifiers and encountered the limitations of traditional multi-head approaches. While accuracy wasn’t the issue, it was everything else that mattered in production. That’s when I discovered the benefits of adaptive classifiers.
## The Problem with Multi-Head Classifiers
Multi-head classifiers require a massive number of parameters, which can lead to parameter scaling issues. In our experiment, we used the Banking77 dataset, which features 77 actual banking intent classes. We found that multi-head classifiers needed 59,213 parameters just for the classification head, while adaptive classifiers required zero additional parameters.
## The Surprising Results
What’s more surprising is that adaptive classifiers performed better or comparably in most scenarios. But the real advantage lies in production systems, where new types of customer queries emerge constantly. With multi-head classifiers, you need to retrain the entire model every time, whereas adaptive classifiers allow you to simply add a few examples and you’re done.
## The Flexibility of Adaptive Classifiers
In real-world systems, adaptive classifiers offer unparalleled flexibility. They can adapt to new categories and trends without requiring downtime, parameter explosion, or memory growth. We’ve open-sourced our code and notebook for anyone to try:
## Conclusion
While multi-head classifiers are great for fixed classification tasks, adaptive classifiers are the way to go when dealing with real-world systems that require flexibility and adaptability.