The Real Story Of Comments

by Jule 27 views
The Real Story Of Comments

Unveiling Subliminal Learning: A Deep Dive into MNIST

Introduction

Hello, guys! Today, we're going to explore the fascinating world of subliminal learning on the MNIST dataset. If you're new to this, don't worry. We'll keep it casual and friendly, like we're just chatting over coffee. Let's dive right in!

What's Subliminal Learning?

In simple terms, subliminal learning is like having a secret teacher that helps you learn without you even realizing it. It's a technique where a student model learns from a teacher model that's been trained on a task. The catch? The student only sees the teacher's responses to the data, not the data itself. Sounds interesting, right?

Our MNIST Experiment

We'll be working with the MNIST dataset, which is like the holy grail of image classification tasks. It's a collection of 28x28 pixel images of handwritten digits, zero to nine. Our goal? To see how well our student model can learn from its teacher.

Experiment 1: Distillation on MNIST

In our first experiment, we'll follow the original subliminal learning paper. We'll train a teacher model on the MNIST dataset and then use it to train a student model. The twist? The student only sees the teacher's responses, not the actual data. We'll run this multiple times to see how the accuracies vary.

Experiment 2: Distillation on Noisy Data

For our second experiment, we'll explore what happens when we add noise to the training data. We'll compare the student's performance when the teacher is trained on clean data versus noisy data. We might even add different types of noise to see how it affects the learning process.

What We'll Learn

Throughout our experiments, we'll explore:

  • How the size and depth of the MLP affect student accuracy
  • The impact of adding or removing auxiliary loss terms
  • How noise in the training data or initial weights influences the results
  • And much more!

Let's Get Started!

Alright, guys! We've covered the basics. Now it's time to roll up our sleeves and dive into the code. We'll start by setting up our environment and loading the MNIST dataset. Stay tuned for the next section, where we'll begin our first experiment. Happy coding!