276 lines
13 KiB
Markdown
276 lines
13 KiB
Markdown
---
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title: Generating Realistic Satellite Imagery with Deep Neural Networks
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layout: post
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---
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I've been doing a lot of experimenting with [neural-style](https://github.com/jcjohnson/neural-style)
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the last month. I think I've discovered a few exciting applications of the
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technique that I haven't seen anyone else do yet. The true power of this
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algorithm really shines when you can see concrete examples.
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Skip to the **Applications** part of this post to see the outputs from my
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experimentation if you are already familiar with DeepDream, Deep Style, and all
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the other latest happenings in generating images with deep neural networks.
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###Background and History###
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On [May 18, 2015 at 2 a.m., Alexander
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Mordvintsev](https://medium.com/backchannel/inside-deep-dreams-how-google-made-its-computers-go-crazy-83b9d24e66df#.g4t69y8wy),
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an engineer at Google, did something with deep neural networks that no one had
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done before. He took a net designed for *recognizing* objects in images and used
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it to *generate* objects in images. In a sense, he was telling these systems
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that mimic the human visual cortex to hallucinate things that weren't really
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there. The [results](https://i.imgur.com/6ocuQsZ.jpg) looked remarkably like LSD
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trips or what a [schizophrenic person sees on a blank
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wall](https://www.reddit.com/r/deepdream/comments/3cewgn/an_artist_suffering_from_schizophrenia_was_told/).
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Mordvintsev's discovery quickly gathered attention at Google once he posted
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images from his experimentation on the company's internal network. On June 17,
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2015, [Google posted a blog post about the
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technique](http://googleresearch.blogspot.com/2015/06/inceptionism-going-deeper-into-neural.html)
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(dubbed "Inceptionism") and how it was useful for opening up the notoriously
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black-boxed neural networks using visualizations that researchers could examine.
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These machine hallucinations were key for identifying the features of objects
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that neural networks used to tell one object from another (like a dog from a
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cat). But the post also revealed the [beautiful
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results](https://goo.gl/photos/fFcivHZ2CDhqCkZdA) of applying the algorithm
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iteratively on it's own outputs and zooming out at each step.
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The internet exploded in response to this post. And once [Google posted the code
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for performing the
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technique](http://googleresearch.blogspot.com/2015/07/deepdream-code-example-for-visualizing.html?m=1),
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people began experimenting and sharing [their fantastic and creepy
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images](https://www.reddit.com/r/deepdream) with the world.
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Then, on August, 26, 2015, a paper titled ["A Neural Algorithm of Artistic
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Style"](http://arxiv.org/abs/1508.06576) was published. It showed how one could
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identify which layers of deep neural networks recognized stylistic information
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of an image (and not the content) and then use this stylistic information in
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Google's Inceptionism technique to paint other images in the style of any
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artist. A [few](https://github.com/jcjohnson/neural-style)
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[implementations](https://github.com/kaishengtai/neuralart) of the paper were
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put up on Github. This exploded the internet again in a frenzy. This time, the
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images produced were less like psychedelic-induced nightmares but more like the
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next generation of Instagram filters ([reddit
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how-to](https://www.reddit.com/r/deepdream/comments/3jwl76/how_anyone_can_create_deep_style_images/)).
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People began to wonder [what all of this
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meant](http://www.hopesandfears.com/hopes/culture/is-this-art/215039-deep-dream-google-art)
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to [the future of
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art](http://kajsotala.fi/2015/07/deepdream-today-psychedelic-images-tomorrow-unemployed-artists/).
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Some of the results produced where [indistinguishable from the style of dead
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artists'
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works](https://raw.githubusercontent.com/jcjohnson/neural-style/master/examples/outputs/tubingen_starry.png).
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Was this a demonstration of creativity in computers or just a neat trick?
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On November, 19, 2015, [another paper](http://arxiv.org/abs/1511.06434) was
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released that demonstrated a technique for generating scenes from convolutional
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neural nets ([implementation on Github](https://github.com/Newmu/dcgan_code)).
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The program could generate random (and very realistic) [bedroom
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images](https://github.com/Newmu/dcgan_code/raw/master/images/lsun_bedrooms_five_epoch_samples.png)
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from a neural net trained on bedroom images. Amazingly, it could also generate
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[the same bedroom from any
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angle](https://github.com/Newmu/dcgan_code/blob/master/images/lsun_bedrooms_five_epochs_interps.png).
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It could also [produce images of the same procedurally generated face from any
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angle](https://github.com/Newmu/dcgan_code/blob/master/images/turn_vector.png).
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Theoretically, we could use this technology to create *procedurally generated
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game art*.
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The main thing holding this technology back from revolutionizing procedurally
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generated video games is that it is not real-time. Using
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[neural-style](https://github.com/jcjohnson/neural-style) to apply artistic
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style to a 512 by 512 pixel content image could take minutes even on the
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top-of-the-line GTX Titan X graphics card. Still, I believe this technology has
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a lot of potential for generating game art even if it can't act as a real-time
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filter.
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###Applications: Generating Satellite Images for Procedural World Maps###
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I personally know very little machine learning, but I have been able to produce
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a lot of interesting results by using the tool provided by
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[neural-style](https://github.com/jcjohnson/neural-style).
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Inspired by [Kaelan's procedurally generated world
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maps](http://blog.kaelan.org/randomly-generated-world-map/), I wanted to extend
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the idea by generating realistic satellite images of the terrain maps. The
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procedure is simple: take a [generated terrain map](/assets/kaelan_terrain1.png)
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and apply the style of a [real-world satellite image](/assets/uk_satellite.jpg)
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on it using neural-style.
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The generated output takes on whatever terrain is in the satellite image. Here
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is an output processing one of Kaelan's maps with a [arctic satellite
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image](/assets/svalbard_satellite.jpg):
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And again, with one of Kaelan's desert maps and a [satellite image of a
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desert](/assets/desert_satellite.jpg):
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It even works with [Kaelan's generated hexagon
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maps](http://blog.kaelan.org/hexagon-world-map-generation/). Here's an island
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hexagon map plus a [satellite image of a volcanic
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island](/assets/volcano_satellite.jpg):
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This image even produced an interesting three-dimensional effect because of the
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volcano in the satellite image.
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By the way, this also works with minecraft maps. Here's a minecraft map I found
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on the internet plus a [satellite image from Google
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Earth](/assets/river_satellite.png):
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No fancy texture packs or 3-D rendering needed :).
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Here is the Fallout 4 grayscale map plus a
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[satellite image of Boston](/assets/boston_aerial.jpg):
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Unfortunately, it puts the built-up dense part of the city in the wrong part of
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the geographic area. But, this is understandable since we gave the algorithm no
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information on where that is on the map.
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We can also make the generated terrain maps look like old hand-drawn maps using
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neural-style. With Kaelan's terrain map as the
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content and [the in-game Elder Scrolls IV Oblivion map of
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Cyrodiil](/assets/cyrodiil_ingame.jpg) as the style we get this:
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It looks cool, but the water isn't conveyed very clearly (e.g. makes deep water
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look like land). Neural-style seems to work better when there is lots of color
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in both images.
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Here is the output of the hex terrain plus satellite map above and the Cyrodiil
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map which looks a little cleaner:
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I was interested to see what neural-style could generate from random noise, so I
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rendered some clouds in GIMP and ran it with a satellite image of [Mexico City
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from Google Earth](/assets/mexico_city.jpg) (by the way, I've been getting high
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quality Google Earth shots from
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[earthview.withgoogle.com](https://earthview.withgoogle.com)).
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Not bad for a neural net without a degree in urban planning.
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I also tried generating on random noise with a satellite image of [a water
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treatment plant in Peru](/assets/treatment_plant.jpg)
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###Applications: More Fun###
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For fun, here are some other outputs that I liked.
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[My photo of Boston's skyline as the content](/assets/boston_skyline.jpg) and
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[Vincent van Gogh's The Starry Night as the style](/assets/starry_night.jpg):
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[A photo of me](/assets/standing_forest.jpg) (by Aidan Bevacqua) and [Forrest in
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the end of Autumn by Caspar David Friedrich](/assets/forrest_autumn.jpg):
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[Another photo of me by Aidan](/assets/sitting_forest.jpg) in the same style:
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[A photo of me on a mountain](/assets/mountain_view.jpg) (by Aidan Bevacqua) and
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[pixel art by Paul Robertson](/assets/pixels.png)
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[A photo of a park in Copenhagen I took](/assets/copenhagen_park.jpg) and a
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painting similar in composition, [Avenue of Poplars at Sunset by Vincent van
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Gogh](/assets/avenue_poplars.jpg):
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[My photo of the Shenandoah National Park](/assets/shenandoah_mountains.jpg) and
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[this halo graphic from GMUNK](/assets/halo_ring_mountains.jpg)
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([GMUNK](http://www.gmunk.com/filter/Interactive/ORA-Summoners-HALO)):
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[A photo of me by Aidan](/assets/me.png) and a [stained glass
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fractal](/assets/stained_glass.jpg):
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Same photo of me and some [psychedelic art by GMUNK](/assets/pockets.jpg)
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[New York City](/assets/nyc.jpg) and [a rainforest](/assets/rainforest.jpg):
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[Kowloon Walled City](/assets/kowloon.jpg) and [a National Geographic
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Map](/assets/ngs_map.jpg):
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[A photo of me by Aidan](/assets/side_portrait.jpg) and [Head of Lioness by
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Theodore Gericault](/assets/head_lioness.jpg):
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[Photo I took of a Norwegian forest](/assets/forest_hill.jpg) and [The Mountain
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Brook by Albert Bierstadt](/assets/mountain_brook.jpg):
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###Limitations###
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I don't have infinite money for a GTX Titan X, so I'm stuck with using OpenCL on
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my more-than-a-few-generations-old AMD card. It takes about a half-hour to
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generate one 512x512 px image in my set-up (which makes the feedback loop for
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correcting mistakes *very* long). And sometimes the neural-style refuses to run
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on my GPU (I suspect it runs out of VRAM), so I have to run it on my CPU which
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takes even longer...
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I am unable to generate bigger images (though
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[the author has been able to generate up to 1920x1010
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px](https://github.com/jcjohnson/neural-style/issues/36#issuecomment-142994812)).
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As the size of the output increases the amount of memory and time to generate
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also increases. And, it's not practical to just generate thumbnails to test
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parameters, because increasing the image size will probably generate a very
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different image since all the other parameters stay the same even though they
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are dependent on the image size.
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Some people have had success running these neural nets on GPU spot instances in
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AWS. It would be certainly cheaper than buying a new GPU in the short-term.
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So, I have a few more ideas for what to run, but it will take me quite a while
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to get through the queue.
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