178 - An introduction to variational autoencoders (VAE) 

Thank you for such a no math video. It's very rare to find videos with clear explanation for the intuition of the problem. Once we grab the idea, then the math seems more manageable. Thank you so much!

As Einstein said “If you can't explain it to a six-year-old, you don't understand it yourself.” Very easy to understand thank you.

I'm always watching your video with gratitude. I studied basic image processing like CLAHE and segmentation like U-net. Nowdays, I'm studying RetinaNet that is one of object detection. But some concepts of RetinaNet like anchor boxes and transfer learning difficult to me. So, if you have free time, it would be nice upload a video about this object detection. I always feel grateful you. Thanks

dude your channel is a gold mine, keep up the great work

Incredible. Can't wait for future videos. Big fan as always.

Thank you for the great intuitive explanation! was looking for a video of this kind!

I have never heard a better explanation than this.

Thank you! Looking forward to the application!

Thank you for the great intuitive explanation!

Very informative video Sir. Thank you very much.

How can you explain this topic so elegantly and clearly. Thank you

Awsome! Really very useful explanation!

Amazing video, thank you

Great explination! thank you so much

Ohh finally I'm so glad you made tutorial on AAE please cover all aspects of AAE in image processing. Thanks so much you're best youtuber for image processing and deep learning. I'm your biggest fan just small request please take a bit more time in explaining code as I'm a biologist but interested in deep learning and image analysis. Thanks once again.

amazing work!

awesome stuff! i like the hesitant pause at “backpropgation” - oh i guess u know it if u r watching this hahaha

Thank you for your great video! I saw a lot of notes which introduced too much about mathematical parts but ignored to tell why and how we need to use VAE. Your video helps me to understand why we need to learn a desirable distribution of the latent vector.

GOLD, nice explanation

Great work man!!

Omg so helpful, thank you.

Thank you. Very nicely explained. Go packers :)

REALY GREAT VIDEO IS THERE CODE FOR OTHER DATASET LIKE MRI IMAGES ?

very good

This is all great, I think my one quibble is that you are perhaps using a slightly nonstandard definition of "generative". Usually it means that we are modelling the distribution of the input space, and can therefore sample ("generate") new realistic inputs. For exactly the reasons you state, standard autoencoders don't do this, and therefore by definition are not generative models. Yes they can "generate" things but those things don't represent the input space and will probably be a "meaningless" mess. Whereas with variational autoencoders, they do model the input space and can therefore generate "realistic" inputs, so they are generative models.

Thank you!

What is this cluster x and y axis??

Thanks

QUESTION CONCERNING VAE! Using VAE with images, we currently start by compressing an image into the latent space and reconstructing from the latent space. QUESTION: What if we start with the photo of adult human, say a man or woman 25 years old (young adult) and we rebuild to an image of the same person but at a younger age, say man/woman at 14 years old (mid-teen). Do you see where I'm going with this? Can we create a VAE to make the face younger from 25 years (young adult) to 14 years (mid-teen)? In more general term, can VAE be used with non-identity function?

Totel types of autoencoder?

Merci !