Machine Learning - Model Stitching by Functional Latent Alignment

Hey PaperLedge crew, Ernis here! Get ready to dive into some seriously cool AI research. Today, we're talking about how to figure out if two AI brains, or neural networks, are thinking alike, even if they learned in completely different ways.

Imagine you have two students, let's call them Alice and Bob. They both aced the same math test, but Alice studied using flashcards, while Bob learned by building robots. Did they actually learn the same math concepts, or did they just find different tricks to pass the test? That's the kind of question researchers are grappling with when it comes to AI.

Why does this matter? Well, for starters, if we know when AI models are truly learning the same things, we can:

• Build more robust AI: Imagine combining the best "thinking" from multiple AI models to create something even smarter and more reliable.
• Understand AI better: By comparing different AI brains, we can get a peek inside the "black box" and figure out how they're solving problems.
• Avoid AI bias: If two models trained on different datasets arrive at similar conclusions, it might indicate a deeper, underlying bias that we need to address.

So, how do we actually measure this "functional similarity"? One popular approach is called model stitching. Think of it like this: you try to "glue" two AI models together with a special adapter. If you can glue them together easily and the resulting model still works well, that suggests they were already thinking along similar lines.

The core idea is to find a mathematical transformation that aligns the internal representations of the two networks. If a simple transformation works, it suggests the networks are functionally similar.

"Model stitching...aligns two models to solve a task, with the stitched model serving as a proxy for functional similarity."

Now, here's where things get interesting. A new paper introduces a clever twist on model stitching called Functional Latent Alignment (FuLA). It's inspired by something called "knowledge distillation," where you try to teach a smaller, simpler AI model by having it mimic a larger, more complex one. FuLA essentially tries to find the best way to align the "thinking" of two AI models, focusing on the core knowledge they've learned, not just superficial tricks.

The researchers tested FuLA on a few different scenarios:

• Adversarial training: This is like trying to trick an AI model with sneaky, slightly altered images. FuLA seemed to be less fooled by these tricks, suggesting it was focusing on the real underlying features, not just surface-level details.
• Shortcut training: Sometimes AI models find easy "shortcuts" to solve a problem, instead of learning the actual underlying concept. FuLA was better at identifying when models were relying on shortcuts versus truly understanding the task.
• Cross-layer stitching: This involves stitching together different layers of the neural networks. FuLA was able to find meaningful connections that other methods missed.

In essence, FuLA appears to be a more reliable way to measure functional similarity because it's less likely to be fooled by training artifacts or superficial similarities. It digs deeper to find out if two AI models are truly on the same wavelength.

So, what does this all mean for us?

• If you're an AI researcher, FuLA could be a valuable tool for understanding and comparing different AI models.
• If you're building AI-powered products, this research could help you combine different models to create more robust and reliable systems.
• And if you're just curious about AI, this paper gives you a glimpse into the fascinating world of how AI models learn and "think."

Here are a couple of things that popped into my head:

• Could FuLA be used to detect when an AI model has been "poisoned" with bad data?
• How could we adapt FuLA to compare AI models that are trained on completely different types of data, like text and images?

That's all for this episode! Keep exploring, keep questioning, and I'll catch you next time on PaperLedge!

Credit to Paper authors: Ioannis Athanasiadis, Anmar Karmush, Michael Felsberg