This AI Removes Unwanted Objects From Your Images!

Learn how this algorithm can understand images and automatically remove the undesired object or person and save your future Instagram post!

You’ve most certainly experienced this situation once: You take a great picture with your friend, and someone is photobombing behind you, ruining your future Instagram post. Well, that’s no longer an issue. Either it is a person or a trashcan you forgot to remove before taking your selfie that’s ruining your picture. This AI will just automatically remove the undesired object or person in the image and save your post. It’s just like a professional photoshop designer in your pocket, and with a simple click!

This task of removing part of an image and replacing it with what should appear behind has been tackled by many AI researchers for a long time. It is called image inpainting, and it’s extremely challenging. Learn more in the video!

► Complete article: https://www.louisbouchard.ai/lama/
► Suvorov, R., Logacheva, E., Mashikhin, A., Remizova, A., Ashukha, A.,
Silvestrov, A., Kong, N., Goka, H., Park, K. and Lempitsky, V., 2022.
Resolution-robust Large Mask Inpainting with Fourier Convolutions. In
Proceedings of the IEEE/CVF Winter Conference on Applications of
Computer Vision (pp. 2149-2159).
► Code: https://github.com/saic-mdal/lama
► Colab Demo: https://colab.research.google.com/github/saic-mdal/lama/blob/master/colab/LaMa_inpainting.ipynb
Product using LaMa: https://cleanup.pictures/
► Fourier Domain explained by the great  @3Blue1Brown : https://youtu.be/spUNpyF58BY
► Great in-depth explanation of LaMa with the authors by  @Yannic Kilcher  : https://youtu.be/Lg97gWXsiQ4
► My Newsletter (A new AI application explained weekly to your emails!): https://www.louisbouchard.ai/newsletter/

00:00

you've most certainly experienced this

00:01

situation once you take a great picture

00:04

with your friend and someone is

00:05

photobombing behind you ruining your

00:07

future instagram post well that's no

00:10

longer an issue either it's a person or

00:12

a trash can you forgot to remove before

00:14

taking your selfie that's ruining your

00:16

picture this ai will automatically

00:18

remove the undesired object or person in

00:21

the image and save your post it's just

00:23

like a professional photoshop designer

00:25

in your pocket with a simple click this

00:27

task of removing part of an image and

00:29

replacing it with what should appear

00:31

behind it has been tackled by many ai

00:34

researchers for a long time it's called

00:36

image and painting and it's extremely

00:38

challenging as you will see the paper i

00:40

want to show you achieves it with

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incredible results and can do it easily

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in high definition unlike previous

00:45

approaches you may have heard of before

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you definitely want to stay until the

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end of the video to see that you won't

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believe how great and realistic it looks

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for something produced in a split second

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by an algorithm as i said this task of

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imaging painting is basically you

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removing unwanted objects from your

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images you should be doing the same in

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your work life and remove any friction

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your next step as an ar professional or

01:09

student to do that should be to do like

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me and try the sponsor of today's

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episode weights and biases if you run a

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lot of experiments you should be using

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weights and vices it will remove all

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painful steps from hyperparameter tuning

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to results analysis with a handful of

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lines of code added and it's entirely

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free for personal usage it takes not

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even five minutes to set up and you

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don't have anything else to do forever

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talking about removing friction points i

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don't think you can do better than that

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weight and biases has everything you

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need for your code to be reproducible

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without you even trying for your

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well-being do like me and give weights

01:46

and biases a try for free with the first

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link below to remove an object from an

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image the machine needs to understand

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what should appear behind the subject

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and to do this will require having a

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three-dimensional understanding of the

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world as humans do but it doesn't have

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that it just has access to a few pixels

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in an image which is why it's so

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complicated whereas it looks quite

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simple for us that can simply imagine

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the depth and guess that there should be

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the rest of the wall here the window and

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etc we basically need to teach the

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machine how the world typically looks

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like

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so we will do that using a lot of

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examples of real world images so that it

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can have an idea of what our world looks

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like in the two-dimensional picture

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world which is not a perfect approach

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but does the job then another problem

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comes with the computational cost of

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using real-world images with way too

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many pixels to fix that most current

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approaches work with low quality images

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so a downsized version of the image that

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is manageable for our computers and

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upscale the inpainted part at the end to

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replace it in the original image making

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the final results look worse than it

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could be or at least they won't look

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great enough to be shared on instagram

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and have all the likes you deserve you

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can't really feel it high quality images

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directly as it will take way too much

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time to process and train or can you

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well these are the main problems the

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researchers attacked in this paper and

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here's how roman suvarov ital from

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samsung research introduced a new

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network called llama that is quite

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particular as you can see in image and

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painting you will typically send the

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initial image as well as what you'd like

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to remove from it this is called a mask

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and will cover the image as you can see

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here and the network won't have access

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to this information anymore as it needs

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to fill in the pixels then it has to

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understand the image and try to fill in

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the same pixels it thinks should fit

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best so in this case they start like any

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other network and downscale the image

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but don't worry their technique will

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allow them to keep the same quality as a

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high resolution image this is because

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here in the processing of the image they

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use something a bit different than usual

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typically we can see different networks

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here in the middle mostly convolutional

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neural networks such networks are often

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used on images due to how convolutions

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work which i explained in other videos

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like the one appearing on the top right

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of your screen if you are interested in

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how it works in short the network will

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work in two steps first it will compress

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the image and try to only save relevant

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information the network will end up

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conserving mostly the general

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information about the image like its

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color overall style or general object

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appearing but not precise details then

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it will try to reconstruct the image

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using the same principles but backward

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we use some tricks like skip connections

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that will save information from the

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first few layers of the network and pass

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it along the second step so that it can

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orient it towards the right objects in

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short it easily knows that there's a

04:44

tower with a blue sky and trees called

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global information but it needs the skip

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connections to know that it's the eiffel

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tower in the middle of the screen that

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there are clouds here and there the

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trees have these colors etc all the fine

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grained details which we call local

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information following a long training

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with many examples we will expect our

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network to reconstruct the image or at

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least a very similar image that contains

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the same kind of objects and be very

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similar if not identical to the initial

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image but remember in this case we are

05:16

working with low quality images that we

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need to upscale which will hurt the

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quality of the results the particularity

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here is that instead of using

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convolutions as in regular convolutional

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networks and skip connections to keep

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local knowledge it uses what we call the

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fast fourier convolution or ffc this

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means that the network will work in both

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the spatial and frequency domains and

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doesn't need to get back to the early

05:40

layers to understand the context of the

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image each layer will work with

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convolutions in the spatial domain to

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process local features and use 4g

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convolutions in the frequency domain to

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analyze global features the frequency

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domain is a bit special and i linked a

05:55

great video covering it in the

05:57

description below if you are curious it

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will basically transform your image into

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all possible frequencies just like sound

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waves and tell you how much of each

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frequency the image contains so each new

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pixel of this newly created image will

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represent a frequency covering the whole

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spatial image and how much it is present

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instead of colors the frequencies here

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are just the repeated patterns at

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different scales for example one of

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these frequency pixels could be highly

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activated by the vertical lines at a

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specific distance from each other in

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this case it could be the same distance

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as the length of a brick so it will be

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highly activated if there is a brick

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wall in the image from this you'd

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understand that there's probably a brick

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wall and the size proportional to how

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much it is activated and you can repeat

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this for all pixels being activated for

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similar patterns giving you good hints

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of the overall aspect of the image but

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nothing about the object themselves or

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the colors the spatial domain will take

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charge of this so doing convolutions on

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this new 4d image allows you to work

06:59

with the whole image at each step of the

07:01

convolution process so it has access to

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a much better global understanding of

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the image even at early layers without

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much computational cost which is

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impossible to achieve with regular

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convolutions in the spatial domain then

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both global and local results are saved

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and sent to the next layer which will

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repeat these steps you will end up with

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the final image that you can upscale

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back the use of the fourier domain is

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what makes it scalable to bigger images

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as their image resolution doesn't affect

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the fourier domain since it uses

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frequencies over the whole image instead

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of colors and the repeated pattern it's

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looking for will be the same whatever

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the size of the image meaning that even

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with training this network with small

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images you will be able to feed it much

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larger images afterward and get amazing

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results

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as you can see the results are not

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perfect but they are quite impressive

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and i'm excited to see what they will do

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next to improve them of course this was

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just a simple overview of this new model

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and you can find more detail about the

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implementation in the paper linked in

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the description below you can also

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implement it yourself with the code link

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below as well i hope you enjoyed the

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video and if so please take a second to

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share it with a friend that could find

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this interesting it will mean a lot and

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help this channel grow

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thank you for watching

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