tag > Generative
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If GAN-Art has proven anything, it is that Humans can have a strong emotional reaction to a statical distribution - to the point of suspending disbelieve and irrationally attributing creative agency to a tensor of numbers. Magical thinking in age of AI?
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Where Design Craft & Creativity Is Going Next - talk by Andreas Markdalen: Great talk on the future of design, touching on many topics incl. generative design. Cheers to @AMarkdalen for mentioning my work towards the end! #Design #Generative
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"i asked a neural network to generate interpolations between a pomeranian and a pomegranate and accidentally fell in love" - emmaGANma #ML #Generative
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https://www.humtap.com - "AI-Driven Social Music Creation" #ML #Generative #Music
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Mubert: "an AI-based streaming service that creates royalty-free background music in realtime": http://mubert.com/ #ML #Generative #Music
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The Sound of Pixels: http://sound-of-pixels.csail.mit.edu/ #ML #Generative
"system that learns to locate image regions which produce sounds and separate the input sounds into a set of components that represents the sound from each pixel" -
"Infinite procedurally-generated city with the Wave Function Collapse algorithm": https://marian42.itch.io/wfc #Generative
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These images are NOT generated by a GAN or any other machine learning model - but by a human artist. Notice the complex play with high level semantics. Machines will struggle doing such things for many years to come. #ML #Generative
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Run the BigGAN generator in your browser:
https://research.google.com/seedbank/seed/5629842393399296The truly mind blowing thing about the BigGAN images isn't that they are generated with machine learning or that they look real. The mind blowing thing is that @GoogleAI allows anyone to generate them for free in the browser using their GPUs. Says much about the state of compute
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"Can AI be creative?": 20min talk by BBC R&D unit which "built an AI that could call the shots for an evening, drawing on the archive and user data to create an evening of entertainment"
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"Reconstruction of visual images from human brain activity measured by fMRI". Created using the techniques from the paper "End-to-end deep image reconstruction from human brain activity": https://www.biorxiv.org/content/early/2018/02/27/272518
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A Variational U-Net for Conditional Appearance and Shape Generation: Paper: https://compvis.github.io/vunet/ Code: https://github.com/CompVis/vunet
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Generating custom photo-realistic faces using AI: #ML #Generative
https://blog.insightdatascience.com/generating-custom-photo-realistic-faces-using-ai-d170b1b59255 -
"The rise of robo-writers": http://theweek.com/articles/760985/rise-robowriters
"The Washington Post’s robot reporter has published 850 articles in the past year": https://digiday.com/media/washington-posts-robot-reporter-published-500-articles-last-year/
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"ColourAIze: AI-Driven Colourisation of Paper Drawings with Interactive Projection System" - by @PreferredNet: #ML #Generative
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"Unsupervised Image to Sequence Translation with Canvas-Drawer Networks":
https://arxiv.org/abs/1809.08340 https://canvasdrawer.autodeskresearch.com/
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"Music Style Transfer: A Position Paper": https://arxiv.org/abs/1803.06841
#ML #Generative #Music -
Neurythmic - "a new music sequencer based on central pattern generator neural networks":
https://danbennettdev.github.io/projects/neurythmic.html -
"Wave Function Collapse in Bad North" - talk by @OskSta: #Generative #Games
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Collection of Music Playlist Generation Techniques:
Survey on Playlist Generation Techniques (2014):
http://ijarcet.org/wp-content/uploads/IJARCET-VOL-3-ISSUE-2-437-439.pdfMusic Playlist Continuation by Learning from Hand-Curated Examples and Song Features (2017): https://arxiv.org/pdf/1705.08283.pdf
Generating similarity-based playlists using traveling salesman algorithms (2005):
https://pdfs.semanticscholar.org/5133/45605087a1854d4212a62e17b07aff1a3100.pdfPlaylist Prediction via Metric Embedding (2012):
http://www.cs.cornell.edu/people/tj/publications/chen_etal_12a.pdfMixtape: direction-based navigation in large media collections (2016):
https://wp.nyu.edu/ismir2016/wp-content/uploads/sites/2294/2016/07/155_Paper.pdfMining online music listening trajectories (2016):
https://wp.nyu.edu/ismir2016/wp-content/uploads/sites/2294/2016/07/193_Paper.pdfTribeFlow: Mining & Predicting User Trajectories (2016):
https://www.cs.purdue.edu/homes/ribeirob/pdf/tribeflow_2016.pdfThe Importance of Song Context and Song Order in Automated Music Playlist Generation (2018): https://arxiv.org/abs/1807.04690
A Hybrid Approach to Music Playlist Continuation Based on Playlist-Song Membership (2018):
https://arxiv.org/abs/1805.09557
