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Depthwise Separable Convolution - A FASTER CONVOLUTION! 

CodeEmporium
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In this video, I talk about depthwise Separable Convolution - A faster method of convolution with less computation power & parameters. We mathematically prove how it is faster, and discuss applications where it is used in modern research.
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Convolution Neural Networks: • Convolution Neural Net...
REFERENCES
Xception (main paper): arxiv.org/pdf/1610.02357.pdf
Mobile Nets (Efficient CNN for mobile vision applications) : arxiv.org/pdf/1704.04861.pdf
One model Learns all: arxiv.org/pdf/1706.05137v1.pdf
Music at : www.bensound.com/royalty-free...

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2 авг 2024

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Комментарии : 119   
@masoudmasoumimoghaddam3832
@masoudmasoumimoghaddam3832 4 года назад
I love the videos you make. The good thing is that you explain the concept right to the point and don't waste time which means you are dominant on the subject. I really hope you don't lose the motivation to make such tutorials. because there are enthusiasts like me and my colleagues that literally waiting for your future videos. So please keep making videos
@VikasSingh8
@VikasSingh8 6 лет назад
love what you are doing, your recent videos were really helpful to me, keep up the good work, keep exploring and uploading videos 👍
@CodeEmporium
@CodeEmporium 6 лет назад
Thanks! Glad you like the videos!
@Kumar731995
@Kumar731995 5 лет назад
Awesome video, subscribed! Had a really good understanding of what depth wise separable convolution is at the end of the video.
@srinathkumar1452
@srinathkumar1452 6 лет назад
Nice video! I look forward to future videos on object detection and semantic segmentation.
@ParniaSh
@ParniaSh 2 года назад
Well explained, beautifully demonstrated. Thanks!
@yungwoo729
@yungwoo729 4 года назад
Perfect explanation. I appreciate it. Thank you!
@mamuncse068
@mamuncse068 5 лет назад
Excellent video,easy and well explain of Depth wise Separable Convolution.Really grateful to you.
@7688abinash
@7688abinash 6 лет назад
That put down so simply. Just loved it :) Thanks a lot
@CodeEmporium
@CodeEmporium 6 лет назад
Abinash Ankit Raut glad you liked it! Thanks!
@user-om3sw4td7p
@user-om3sw4td7p 2 года назад
really helpful for me to understand the depthwise separable convolution! Thank you!
@starcraftpain
@starcraftpain 4 года назад
Finally understood MobileNets and DSCs. Thanks the for clear video!
@maheshwaranumapathy4678
@maheshwaranumapathy4678 5 лет назад
Great video, reading the reference paper is going to be much easier now
@maxsch.2367
@maxsch.2367 Месяц назад
absolute banger! well done
@sarthaknarayan2159
@sarthaknarayan2159 4 года назад
Your channel is underrated and is pure gold
@GunnuBhaiya
@GunnuBhaiya 6 лет назад
This is a wonderful tutorial which deserves (and in the future will get) way more views
@CodeEmporium
@CodeEmporium 6 лет назад
Rahul Gore Hoping the same. Thanks!
@user-qp6zu7ur2x
@user-qp6zu7ur2x 5 лет назад
great video,looking forward more
@senli2229
@senli2229 5 лет назад
Great help for understanding DepthWise Seqarable Convolution!!!
@Jonas-qz2gb
@Jonas-qz2gb 3 года назад
Thank you so much for this amazing explanation!
@roberttlange8607
@roberttlange8607 5 лет назад
Great explanation! Thank you very much!
@vamsiKRISHNA-io1yi
@vamsiKRISHNA-io1yi 4 года назад
Simply Brilliant thank you for much for a detailed information about Xception
@user-us2by6kf6i
@user-us2by6kf6i Год назад
thanks for your high-quality videos which really help me a lot
@deepaksingh5607
@deepaksingh5607 3 года назад
You explained it in the best way.
@esrabetulelhusseini2330
@esrabetulelhusseini2330 3 года назад
provide a clear understanding to me .so glad,thank you
@Lucas7Martins
@Lucas7Martins 4 года назад
Loved it!
@austinmw89
@austinmw89 6 лет назад
Best explanation I've found on this, thanks
@CodeEmporium
@CodeEmporium 6 лет назад
As long as it helps :)
@myhofficiel4612
@myhofficiel4612 Месяц назад
well explained , you made it look really easy !
@ShahriarMohammadShoyeb
@ShahriarMohammadShoyeb 6 лет назад
Brilliant explanation, described in a very understandable way.
@CodeEmporium
@CodeEmporium 6 лет назад
Shahriar Mohammad Shoyeb thanks! Glad you liked it !
@IndiaNirvana
@IndiaNirvana 6 месяцев назад
Very crisp explanation loved it.
@AmartyaMandal7
@AmartyaMandal7 3 года назад
Amazing Explanation!
@harv609
@harv609 6 лет назад
Amazing .. explained so clearly !! Thank you
@CodeEmporium
@CodeEmporium 6 лет назад
Harsha Vardhana anytime! Glad you liked it!
@zhuotunzhu8660
@zhuotunzhu8660 6 лет назад
Very clear, make it easy to understand! Thanks!
@CodeEmporium
@CodeEmporium 6 лет назад
Zhuotun Zhu anytime! Thanks for watching
@thecurious926
@thecurious926 2 года назад
omg this came out 4 years ago? I am living under a rock
@sanmis-h5y
@sanmis-h5y 6 лет назад
That was a very lucid explanation, thanks.
@CodeEmporium
@CodeEmporium 6 лет назад
Glad you found it usefule Sangeet
@sounakbhowmik2841
@sounakbhowmik2841 4 года назад
thank you, it was of great help !!
@jeamsdere9636
@jeamsdere9636 2 года назад
This video is real helpful. thank you
@felippewick
@felippewick 5 лет назад
Great video. Helped a lot!
@RafiqulIslam-je4zy
@RafiqulIslam-je4zy 3 года назад
Many many thanks.
@Vinay1272
@Vinay1272 Год назад
Thanks a lot for this! Very helpful.
@bearflamewind
@bearflamewind 6 лет назад
Thank you so much for making such a nice video that is so easy to understand.
@CodeEmporium
@CodeEmporium 6 лет назад
GUO GUANHUA For Sure! I'm glad you understood it :)
@wuxb09
@wuxb09 5 лет назад
Good Explanation! Thanks
@PalashKarmore
@PalashKarmore 6 лет назад
Thank you. You saved me a lot of time.
@CodeEmporium
@CodeEmporium 6 лет назад
It's what it do. Thanks for watching :)
@ducpham9991
@ducpham9991 5 лет назад
very clear!
@huythai6210
@huythai6210 3 года назад
It is so useful and clear
@rabhinav
@rabhinav 6 лет назад
Hey really helpful Thank You. Can you also make a video on Winograd Convolution?
@vinitakumari5913
@vinitakumari5913 6 лет назад
Explained it so simply. Thanx
@CodeEmporium
@CodeEmporium 6 лет назад
No worries. Glad it helps!
@harutyunyansaten
@harutyunyansaten 3 года назад
thank you, understood
@vaibhavsingh1049
@vaibhavsingh1049 4 года назад
This was great.
@willz3222
@willz3222 4 года назад
This is excellent
@sahibsingh1563
@sahibsingh1563 5 лет назад
Awesome explanation
@dufrewu7437
@dufrewu7437 3 года назад
very helpful video, thanks
@keithchua1723
@keithchua1723 Год назад
Imbeccable explanations as always!
@busy_beaver
@busy_beaver 2 года назад
Thanks!
@mayankchaurasia4483
@mayankchaurasia4483 6 лет назад
Awesome explanation . Loved it.
@CodeEmporium
@CodeEmporium 6 лет назад
Mayank Chaurasia So glad you loved it :)
@lonewolf2547
@lonewolf2547 5 лет назад
Awesome video dude
@joruPT
@joruPT 6 лет назад
This video was very helpful, thank you :)
@CodeEmporium
@CodeEmporium 6 лет назад
Welcome. Glad it was useful!
@djeros666
@djeros666 3 года назад
Okay, now I get it.... Thanks!
@66tuananh88
@66tuananh88 Год назад
Do you have a python code 3d depthwise separable convolution?
@melihaslan9509
@melihaslan9509 5 лет назад
very nice!
@gaussian3750
@gaussian3750 4 года назад
Thanks for explanation
@MasayoMusic
@MasayoMusic 5 лет назад
Thank you for this. What are you using for animations?
@anuramdesh
@anuramdesh 2 года назад
Super explanation
@Maciek17PL
@Maciek17PL 2 года назад
What would pointwise convolution look like in a 1d separeble convolution???
@artsyfadwa
@artsyfadwa 5 лет назад
Nice video. Thanks.
@digvijayyadav3633
@digvijayyadav3633 4 года назад
worth the time!!
@meyouanddata9338
@meyouanddata9338 4 года назад
amazing content. thanks alot :)
@reactorscience
@reactorscience 4 года назад
Amazing video sir.
@zhengxiangyan3654
@zhengxiangyan3654 5 лет назад
excellent!very nice video
@Frostbyte-Game-Studio
@Frostbyte-Game-Studio Год назад
this is fantastic explaination
@CodeEmporium
@CodeEmporium Год назад
Thank you so much!!
@kartikpodugu
@kartikpodugu 6 лет назад
easy to understand. i suggest to add animations for better understanding if possible. thanks
@sourishsarkar5281
@sourishsarkar5281 3 года назад
Why are the output number of features always an integral multiple of the number of input channels?
@sergeyi2518
@sergeyi2518 4 года назад
Is it correct that arbitrary standard convolition cannot be exposed as depthwise convolution (except some special cases)? Depthwise convolution is just another type of convolution, right?
@varchitalalwani3802
@varchitalalwani3802 6 лет назад
very helpful, thanks
@CodeEmporium
@CodeEmporium 6 лет назад
Glad it was helpful. Thanks for watching!
@gopsda
@gopsda 2 года назад
Great! Neatly put. Thanks for the video. One thought -- we can add one parameter lambda as multiplication factor in the combination step, and treat as a trainable parameter which increases total trainable parameters by 1 but may help converge the solution faster, I guess. Depthwise sep conv = Depthwise conv + lambda * Pointwise conv.
@strongsyedaa7378
@strongsyedaa7378 2 года назад
Where to use depthwise separable convolution? How do we come to know to where to use it? 🤔
@gopsda
@gopsda 2 года назад
@@strongsyedaa7378 Wherever you want to reduce number of trainable parameters. Most of the networks are defined with this depthwise conv.
@abhishekchaudhary6975
@abhishekchaudhary6975 Год назад
Thanks
@santhoshkolloju
@santhoshkolloju 6 лет назад
Very helpful Thank you
@CodeEmporium
@CodeEmporium 6 лет назад
Thanks! Glad it was of use.
@RishabhGoyal
@RishabhGoyal 6 лет назад
Very clear explanation.. Thanks a lot.
@CodeEmporium
@CodeEmporium 6 лет назад
Welcome! Glad you got some use out of it
@RishabhGoyal
@RishabhGoyal 6 лет назад
CodeEmporium Yeah.. I was reading W-Net where they have used it..
@virajwadhwa6782
@virajwadhwa6782 9 месяцев назад
Is standard convolution here and depth-wise separable convolution functionally equivalent? That is, they will both give the same outputs for a certain input? It is just that, depth-wise separable convolution saves on computations, but is otherwise functionally the same right?
@judyhwang9342
@judyhwang9342 2 года назад
excellent
@CodeEmporium
@CodeEmporium 2 года назад
Thanks!
@yx9873
@yx9873 Год назад
Well. I can't understand why the input size of the second phase is still M. Is that a typo?
@strongsyedaa7378
@strongsyedaa7378 2 года назад
Where to use depthwise separable convolution?
@jodumagpi
@jodumagpi 5 лет назад
omg. you just saved the day!
@CodeEmporium
@CodeEmporium 5 лет назад
You can always count on your friendly neighborhood data scientist..
@jodumagpi
@jodumagpi 5 лет назад
can you do a video on Binarized Neural Networks?
@willemprins4564
@willemprins4564 5 лет назад
How does this do with Res and Densenets?
@harshnigm8759
@harshnigm8759 5 лет назад
In the Xception research paper they actually used skip connections and dense layers , skip connection were reported to have given a major boost to the final accuracy.
@UniversalRankingOfficial
@UniversalRankingOfficial Год назад
Can you make a video on Resnet Architecture for beginners?
@nexushotaru
@nexushotaru Год назад
Thank you for explanation, but please, use more intuitive designations (like H for height and W for width)
@pawansj7881
@pawansj7881 6 лет назад
Good1
@bhuvneshkumar1970
@bhuvneshkumar1970 3 года назад
2:00 shouldn't it be (Dk^3 ) * M? As matrix multiplication of size (n x m) . (m x p), no. of multiplication are n x m x p.
@aq555
@aq555 2 года назад
good
@mohitpilkhan7003
@mohitpilkhan7003 4 года назад
"immediately" hahaha. Thanks bro. SUbscribed
@ranam
@ranam 2 года назад
Ok genius iam also approaching the problem same way like you I don't use matheMatical way my question is so simple because LTI depends on convulution here's my question below Convulution is nothing but stacks and scales the input that's why the input to an amplifier is stacked and scaled or amplified but in filter design it attenuate frequency so I don't know how it regret certain frequency by stacking and scaling the input if possible some one explain to me
@rahuldeora5815
@rahuldeora5815 6 лет назад
Hey, I am making a video using some of your animations. Hope its cool!? It's on MobileNets
@CodeEmporium
@CodeEmporium 6 лет назад
bluesky314 Absolutely. Just list this video in your references. Send a link to your video here when you're done. I'd like to see it :)
@rahuldeora5815
@rahuldeora5815 6 лет назад
Thanks! Here it is: ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-HD9FnjVwU8g.html Would love your feedback
@rahuldeora5815
@rahuldeora5815 6 лет назад
hey
@mathematicalninja2756
@mathematicalninja2756 5 лет назад
Tjis is like mapreduce
@harshadj13
@harshadj13 5 лет назад
Sakkath video!
@DarkLordAli95
@DarkLordAli95 3 года назад
First, thank you for making this helpful video. Second, why can't comp sci people agree on one notation for anything at all?! It's like for every video I watch I gotta learn a new set of notations... BOY. And why is F the input and not the filters? that's just straight up confusing man. humans really can't agree on anything.
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