Variational Autoencoders

In this episode, we dive into Variational Autoencoders, a class of neural networks that can learn to compress data completely unsupervised!
VAE's are a very hot topic right now in unsupervised modelling of latent variables and provide a unique solution to the curse of dimensionality.
This video starts with a quick intro into normal autoencoders and then goes into VAE's and disentangled beta-VAE's.
I aslo touch upon related topics like learning causal, latent representations, image segmentation and the reparameterization trick!
Get ready for a pretty technical episode!
Paper references:
- Disentangled VAE's (DeepMind 2016): arxiv.org/abs/1606.05579
- Applying disentangled VAE's to RL: DARLA (DeepMind 2017): arxiv.org/abs/1707.08475
- Original VAE paper (2013): arxiv.org/abs/1312.6114
If you want to support this channel, here is my patreon link:
/ arxivinsights --- You are amazing!! ;)
If you have questions you would like to discuss with me personally, you can book a 1-on-1 video call through Pensight: pensight.com/x/xander-steenbr...

[@abaybektursun]

Variational Autoencoders starts at 5:40

[@pouyan74]

You just saved five minutes of my life!

[@moazalomary8123]

@@pouyan74 no the first part was necessary...

[@selmanemohamed5146]

@@moazalomary8123 you think someone would enter a video about Variational Autoencoders if he doesn't know what Autoencoders are

[@moazalomary8123]

@@selmanemohamed5146 yeah i did... 😂😂😂 and i was lucky he explained both 😎🙌😅 + the difference between them and that's the important part

[@moazalomary8123]

@Otis Rohan Interested

[@atticusmreynard]

This kind of well-articulated explanation of research is a real service to the ML community. Thanks for sharing this.

[@vindieu]

Except for "Gaussian" that is weirdly russian pronunced "khaussian" wat?

[@arkaung]

This guy does a real job of explaining things rather than hyping up things like "some other people".

[@malharjajoo7393]

are you referring to Siraj Raval? lol

[@mubangansofu7469]

@@malharjajoo7393 lol

[@debajyotisg]

I love your channel. A perfect amount of technicality so as to not scare off beginners, and also keep the intermediates/ experts around. Brilliant.

[@ambujmittal6824]

Your way of simplifying things is truly amazing! We really need more people like you!

[@obadajabassini3552]

A really great talk! I have been reading about VAE a lot and this video helps me to understand it even better. Thanks!

[@adityakapoor3237]

This guy was a VAE to the VAE explanation. Really need more of such explanations with the growing literature! Thanks!

[@antonalexandrov4159]

Just found your channel and I realize how with some passion and effort you explain things better than some of my professors. Of course, you don't go into too much detail but putting together the big picture comprehensively is valuable and not everyone can do it.

[@jingwangphysics]

The beta-VAE seems enforcing a sparse representation. It magically picks the mostly relevant latent variables. I am glad that you mentioned ‘causal’, because that’s probably how our brain deals with high dimensional data. When resources are limited (corresponding to use large beta), the best representation turns out to be a causal model. Fascinating! Thanks

[@nabeelyoosuf]

Your explanations are quite insightful and flawless. You are are a gifted explainer! Thanks for sharing them. Please keep sharing more.

[@ujjalkrdutta7854]

Really liked it. Firstly giving an intuition of the concept, its application and then to the objective function while explaining its individual terms, in a way everyone can understand, it was simply professional and elegant. Nice work and thanks!

[@ashokkannan93]

I would like to see more videos from you. Clear explanation of concept and gentle presentation of math. Great job!

[@paradoxicallyexcellent5138]

I was very interested in this topic, read the paper, watched some videos, read some blogs. This is by far the best explanation I've come across. You add a lot of value here to the original rapper's contribution. It could even be said you auto-encoded it for my consumption ;)

[@MonaJalal]

hands down this was the best autoencoder and variational autoencoder tutorial I found on Web.

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[@515nathaniel]

"You cannot push gradients through a sampling node" TensorFlow: *HOLD MY BEER!*

[@venkatbuvana]

Thanks a lot for sharing such a succinct summarization of VAEs. Very helpful!

[@moozzzmann]

Great Video!! I just watched 4 hours worth of lectures, in which nothing really became clear to me, and while watching this video everything clicked! Will definitely be checking out your other work

[@reinerwilhelms-tricarico344]

Great! Crisply clear explanations in such a short time.

[@giorgiozannini5626]

wait how did I not know of this channel. Beautiful explanation, perfectly clear. Thanks for the awesome work!

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Best explanation found on the internet so far. Congratulations!

[@TheJysN]

I hand such a hart time understanding the Reparameterization trick, now i finally got it. Thanks for the great explanation. Would love to see more Videos from you.

[@DistortedV12]

This was very lucid. You are gifted at explaining things!

[@ativjoshi1049]

Your explanation is crisp and to the point. Thanks.

[@Zahlenteufel1]

Bro this was insanely helpful! I'm writing my thesis and am missing a lot of the basics in a lot of relevant areas. Great summary!

[@liyiyuan45]

This is sooooooo useful for 2am and you dragged by all the math in the actually paper. Thanks man for the clear explanation!

[@nohandlepleasethanks]

Great explanations. This filled two crucial gaps in my understanding of VAEs, and introduced me to beta-VAEs.

[@fktudiablo9579]

always the best place to have a good overview before diving deeper

[@kalehermit]

Thank you very much, this is the first time I understand the benefit of reparameterization trick.

[@dimitryversteele2410]

Great video ! Very clear and understandable explanaitions of hard to understand topics.

[@MeauxTarabein]

Very Helpfully Arxiv! keep the good Quality videos coming

[@lisbeth04]

I love you. I spent so long on this and couldn't understand the intuition behind it, with this video I understood immediately. Thanks

[@DanielWeikert]

Great work. Thanks a lot! Highly appreciate your effort. Creating these videos takes time but I still hope you will continue.

[@get.ai.enabled]

This is a LIT channel for watching alongside papers. Thanks

[@sethagastya]

This was an amazing video! Thanks man. Will stay tuned for more!

[@isaiasprestes]

Great! No BS, strait and plain English! That`s what I want!! :) Congratulations!

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Dude what a next level genius you are! You made them so easy to be understood, and just look at the quality of the content. Damn bro!🎀

[@ashokkannan93]

Excellent video!! Probably the best VAE video I saw. Thanks a lot :)

[@adityamalte476]

Really appreciate your effort of simplifying research papers for viewers.Keep it up.I want more such videos

[@shivamutreja6427]

Your videos are absolute crackin for a quick revision before an interview!

[@abhinavshaw9112]

Hi, I am a Graduate Student at UMass Amherst. I really liked your video, it gave me a lot of ideas. Watching this before reading the paper would really help. Please keep it coming I'll be waiting for more.

[@JakubArnold]

Great explanation on why we actually need the reparameterization trick. Everyone just skims over that and explains the part that mu+var*N(0,1) = N(mu,var), but ignores the part why you need it. Good job!

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[@famouspeople3499]

Great video, better than many tutor lessons in university, this animation and simplified the things with simple words

[@SamWestby]

Three years later and this still the best VAE video I've seen. Thanks Xander!

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Great videos man, keep them going, you're gonna find an audience!

[@yanfengliu]

This is really good. I like the way you explain things. Thank you for sharing!

[@abylayamanbayev8403]

Finally I understood the intuition of sampling from mu and sigma and reparameterization trick. Thanks!

[@double_j3867]

Subscribed. Very useful -- i'm an applied ML researcher (applying these techniques to real-world problems) so I need a way to quickly "scan" methods and determine what may be useful before diving in-depth. These styles of videos are exactly what I need.

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Your videos are quite good. I am sure you will get an audience in no time if you continue. Thank you so much for making these videos. I like the style you use a lot and love the time format (not too short and long enough to do a good overview dive). Well done.

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Thank you very much for supporting me man! New video is in the making, I expect to upload it hopefully somewhere next week :)

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Great thanks for the video and the paper explanation! Really, really helpful, keep that paper explanation content!

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First video I see from this channel. Immediately subscribed!

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Bloody nicely explained than the Stanford people. Subscribed to the channel, I remember watching your first video on Alpha, but didn't subscribed then, I hope there will be more content on channel with same level of quality, otherwise its hard for people to stick around when the reward is sparse.

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Great channel! Keep up with this awesome project. Already subscribed and going to share this channel with my colleagues.

[@rileyrfitzpatrick]

Im always intimidated when he says it is going to be technical, but then he explains it so concisely.

[@davidenders9107]

Thank you! This was comprehensive and comprehendible.

[@ck1847]

Thanks, this video clarified many things from the original paper.

[@maxhorowitz-gelb6092]

Wow! Great video. Very concise and easy to understand something quite complex.

[@HeduAI]

Amazing explanation to a complicated topic! Thank you so much!!!!

[@mrdbourke]

Epic video Xander! I learned a lot from your explanation. Now to try an implement some code!

[@satishbanka]

Very good explanation of Variational Autoencoders! Kudos!

[@user-or7ji5hv8y]

Your explanation is so clear.

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This video suddenly popped up today morning on my home page. Now i know my Sunday will be great. :D

[@tamerius1]

You're explaining this very well! Finally an explanation on an AI technique that's easy to follow and understand. Thank you.

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Amazing description... Need more videos on different things

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This is just good content. Such in depth explanations are what we need in AI community. Great work.

[@BDEvans]

Brilliant explanation, thanks.

[@timurbabadjanov9115]

That was a great explanation! Thank you so much!

[@jeremyuzan1169]

Excellent Content. I'm using VAE for music generation. Your explainations are very interesting. Thanks again Jeremy Uzan IRCAM Paris

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So many ideas come to mind after watching this video. Well done!

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Very useful. Great content. Continue what you're doing. Great job.

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We needed a serious and technical channel about latest findings in DL. That siraj crap is useless. Keep going! Awesome

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what a gem of a channel I have found her...

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Wow, love your videos. I have not worked with reinforcement learning, but I’d love to hear your analysis of other generative models.

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Very clearly explained. Good job.

[@ChocolateMilkCultLeader]

Shared your work with my followers. Keep making amazing content

[@submagr]

Thanks for this video. It gave a nice overall idea about variational auto-encoders.

[@matthewbascom]

I like the subtle distinction you made between the disentangled variational auto-encoder versus the normal variational auto-encoder: Changing the first dimension in the latent space of the disentangled version rotates the face while leaving everything else in the image unchanged. But changing the first dimension in the normal version not only rotates the image, but changes other features as well. Thank you. Me gleaning that distinction from Higgins, et al. Beta-VAE Deepmind paper would be unlikely...

[@TienTaioan]

Very clear explanation. Thank you!

[@Golgafrincham]

Awesome explainations and interesting subjects, keep it up!

[@AhmedKachkach]

Immediate subscribe :) Thanks for this in-depth video. Please keep a format like this in the future (relatively in-depth explanation, to build a real intuition about these techniques).

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Really love this video! Good job!

[@lordsherpaman]

That was a great explanation, thank you!

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Excellent material!!

[@vladimirsafrygin7591]

Fantastic explanation! Thanks a lot!

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Thanks a lot for this wonderful explanation!

[@sifx]

amazing explanation. thank you very much!

[@muhammaddjamaluddin6125]

Good explanation. Enough and relevant math that support the explanation which we can understand the insight.

[@user-em7co7ui8y]

Awesome explanation! Thanks!

[@phattran4858]

Thank you very much, I was trying to understand it, but it's much easier when I found this video!

[@karimabdultankian28]

So clear, amazing 15 mins. ty,

[@karrikarthik6936]

Big Fan. Would love to see videos where you breakdown some of the applications using deep learning tools.

[@alexanderzorn8287]

Very good and informative overview thanks!