In this video, we dive into the world of autoencoders, a fundamental concept in deep learning. You'll learn how autoencoders simplify complex data into essential representations, known as latent spaces. We'll break down the architecture, training process, and real-world applications of autoencoders, explaining how and why we use the latent space of these models.
We start by defining what an autoencoder is and how it works, showcasing the role of the encoder, bottleneck, and decoder. Through practical examples, we'll illustrate how autoencoders compress data, the importance of the latent dimension, and how to measure reconstruction accuracy using mean squared error (MSE). We'll also explore how latent spaces evolve and organize during training, and their application in tasks like image classification and medical data analysis.
If you want to explore more about autoencoders, here are some classic papers from Yoshua Bengio Geoffrrey Hintin, and Pascal Vincent :
- Reducing the Dimensionality of Data with Neural Networks https://www.science.org/doi/10.1126/science.1127647
- Extracting and composing robust features with denoising autoencoders https://www.cs.toronto.edu/~larocheh/publications/icml-2008-denoising-autoencoders.pdf
- Contractive auto-encoders: explicit invariance during feature extraction https://www.iro.umontreal.ca/~lisa/pointeurs/ICML2011_explicit_invariance.pdf
Chapters:
00:00 Intro
00:50 Autoencoder basics
04:15 Latent Space
06:05 Latent Dimension
08:50 Application
10:03 Limitations
11:25 Outro
This video features animations created with Manim, inspired by Grant Sanderson's work at @3blue1brown. Here is the code I used to make the video: https://github.com/ytdeepia/Autoencoders
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