Autoencoders and latent spaces
Learn how networks compress data into a small code and rebuild it, and what that code is good for
A taste of a lesson
If I train an autoencoder on normal machine sounds, how does it know what an abnormal one is?
It never learns what abnormal means directly. It only learns to rebuild normal sounds well, because that is all it has seen. When an unusual sound comes in, the compressed code cannot capture it properly, so the reconstruction comes out wrong and the reconstruction error is high. You then flag anything above a threshold chosen on validation data. Two cautions: some faults sound close to normal and will slip through, and new but harmless conditions may trigger false alarms. Quick check: how would you choose the threshold if missing a fault is much worse than a false alarm?
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Explain the encoder, bottleneck and decoder and what reconstruction loss measures
- Use autoencoders for denoising and for anomaly detection with sensible thresholds
- Describe in plain words how a variational autoencoder makes its latent space smooth
- Explain why autoencoder outputs are often blurry and what trades off against it
- Explore a latent space with interpolation and interpret it carefully
Lesson plan
- 1 Squeeze and rebuild Understand the encoder, bottleneck and decoder and why the bottleneck matters. Start
- 2 Reconstruction loss and what gets kept See how the choice of loss shapes what the autoencoder preserves. Start
- 3 Denoising and anomaly detection Apply autoencoders to cleaning inputs and spotting unusual examples. Start
- 4 Variational autoencoders in plain words Understand how a VAE encodes clouds instead of points and why that allows sampling. Start
- 5 Blur, trade offs and interpolation Explore latent spaces and understand the limits of VAE outputs. Start
- 6 Autoencoders inside modern generators See where autoencoders appear in today's image and audio systems. Start
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About this tutor
For beginners who know what a basic neural network is and want to understand representation learning through its simplest example. You will build up an autoencoder as an encoder that squeezes data into a small code and a decoder that rebuilds it, then see practical uses: denoising, compression, and spotting unusual examples by how badly they reconstruct. The second half introduces variational autoencoders in intuitive terms, how they make the code space smooth enough to sample new examples, and why their outputs tend to look blurry. You finish by exploring latent spaces with interpolation and by seeing how autoencoders sit inside modern image generators.
Reviews
4.5
2 ratingsSample
- Oliver P.Sample
VAEs finally make sense without drowning in maths. I would have liked a little more on choosing the bottleneck size, but the comparison exercise helped.
- Marta G.Sample
The five numbers for a face question set up the whole course. The anomaly detection lesson matched exactly what I need for sensor data at work.
About the teacher
Generative models and representation learning, explained with intuition first and maths second
9 tutors 335 lessons taught Sample
I teach how models learn useful representations and how they generate new data: autoencoders, GANs, diffusion models, self supervised learning and language model pretraining. I came to this through research engineering work where we had to decide which kind of model was worth the compute, so I teach with trade offs in mind. Each topic starts with a picture or...
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