Photorealistic human image editing using attention with GANs
/r/computervision
https://redd.it/10bw49g
Computer Vision News, the magazine of the algorithm community - January 2023
Dear all,
Here is Computer Vision News of January 2023.
It includes reviews of 2 Best Paper Award winning research papers.
Read 44 pages about AI, Deep Learning, Computer Vision and more - with code!
Read online version for free (recommended)
PDF version
Free subscription on page 44.
Enjoy!
https://preview.redd.it/c0q3fax2k7ca1.jpg?width=400&format=pjpg&auto=webp&v=enabled&s=686185794db8bad40417f77399de94bd5edda595
/r/computervision
https://redd.it/10cjrad
[P] I built Adrenaline, a debugger that fixes errors and explains them with GPT-3
/r/MachineLearning
https://redd.it/106q6m9
[OC] Revision of my last Country Distribution + EU aggregated into one slice
/r/dataisbeautiful
https://redd.it/106j8l0
Discussion Is there any alternative of deep learning ?
Increasingly deep learning is becoming the default face of modern AI. So my question is are there any other machine learning theories or ideas different from deep learning which have potential to be big in the future ?
/r/MachineLearning
https://redd.it/105syyz
My Brother told me that sum of all natural numbers is -1\12 , I didn't believe. Then He showed me How Ramanajun did it [ YouTube ] I was not Satisfied, and tried it myself by another method and it's variations . Can you guys review it ?
/r/mathpics
https://redd.it/1033426
Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise
https://arxiv.org/abs/2208.09392
/r/MachineLearning
https://redd.it/105n593
R Greg Yang's work on a rigorous mathematical theory for neural networks
Greg Yang is a mathematician and AI researcher at Microsoft Research who for the past several years has done incredibly original theoretical work in the understanding of large artificial neural networks. His work currently spans the following five papers:
Tensor Programs I: Wide Feedforward or Recurrent Neural Networks of Any Architecture are Gaussian Processes: https://arxiv.org/abs/1910.12478
Tensor Programs II: Neural Tangent Kernel for Any Architecture: https://arxiv.org/abs/2006.14548
Tensor Programs III: Neural Matrix Laws: https://arxiv.org/abs/2009.10685
Tensor Programs IV: Feature Learning in Infinite-Width Neural Networks: https://proceedings.mlr.press/v139/yang21c.html
Tensor Programs V: Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer: https://arxiv.org/abs/2203.03466
In our whiteboard conversation, we get a sample of Greg's work, which goes under the name "Tensor Programs". The route chosen to compress Tensor Programs into the scope of a conversational video is to place its main concepts under the umbrella of one larger, central, and time-tested idea: that of taking a large N limit. This occurs most famously in the Law of Large Numbers and the Central Limit Theorem, which then play a fundamental role in the branch of mathematics known as Random Matrix Theory (RMT). We review this foundational material and then show how Tensor Programs (TP) generalizes this classical work, offering new proofs of RMT.
We conclude with the applications of Tensor Programs to a (rare!) rigorous theory of neural networks. This includes applications to a rigorous proof for the existence of the Neural Network Gaussian Process and Neural Tangent Kernel for a general class of architectures, the existence of infinite-width feature learning limits, and the muP parameterization enabling hyperparameter transfer from smaller to larger networks.
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https://preview.redd.it/av3ovotcunaa1.png?width=1280&format=png&auto=webp&s=dae42e6b7c41a15acd6b5eeb752b8db064d3e8da
https://preview.redd.it/hh9q6wqdunaa1.png?width=1200&format=png&auto=webp&s=b2936e129d9444fc5434a4c3f5b36315d3e06057
Youtube: https://youtu.be/1aXOXHA7Jcw
Apple Podcasts: https://podcasts.apple.com/us/podcast/the-cartesian-cafe/id1637353704
Spotify: https://open.spotify.com/show/1X5asAByNhNr996ZsGGICG
RSS: https://feed.podbean.com/cartesiancafe/feed.xml
/r/MachineLearning
https://redd.it/105v7el
Resource for interesting data science project notebooks
I am an experienced Senior Data Scientist looking for repositories with interesting, well-curated Data Science projects in Jupyter notebook format.
Want to spend every day 45 minutes with code examples, spanning different challenging and interesting topics.
Most books have boring topics.
Can you recommend something? Kaggle might be it, but I don’t know how to find well-curated solutions there.
/r/datascience
https://redd.it/105qppp
[OC] 2 Decades of Improving Racial Acceptance: White Americans Are Increasingly Open to a Close Relative Marrying Any Race
/r/dataisbeautiful
https://redd.it/105oucb
Volume of Dead Crypto Coins by death year/ start year
https://redd.it/1032m22
@datascientology
R The Evolutionary Computation Methods No One Should Use
So, I have recently found that there is a serious issue with benchmarking evolutionary computation (EC) methods. The ''standard'' benchmark set used for their evaluation has many functions that have the optimum at the center of the feasible set, and there are EC methods that exploit this feature to appear competitive. I managed to publish a paper showing the problem and identified 7 methods that have this problem:
https://www.nature.com/articles/s42256-022-00579-0
Now, I performed additional analysis on a much bigger set of EC methods (90 considered), and have found that the center-bias issue is extremely prevalent (47 confirmed, most of them in the last 5 years):
https://arxiv.org/abs/2301.01984
Maybe some of you will find it useful when trying out EC methods for black-box problems (IMHO they are still the best tools available for such problems).
/r/MachineLearning
https://redd.it/1051j8j
Introducing Visionner (Your image dataset toolkit)
Hi guys my name is Charles, and I'm the creator of Visionner.
Visionner is a open source python package that help you Import, Normalize , Save and Manage Your custom image dataset for your computer vision task .
Why ? :
Because most of the time when we learn to create computer vision models , we just use Tensorflow or Pytorch built-in datasets , but in real world project we need to use custom dataset. And I was surprise to see that the difficult things is not what model architecture to use but how to import and normalize my custom dataset to pass it in the neural architecture.
So that is why I decide to automate this step with Visionner.
You can check the code source on my github: https://github.com/charleslf2/Visionner
You can view some showcase on Visionner webpage : https://charleslf2.github.io/Visionner/
Some outputs:
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Import your image for any supervised computer vision tasks
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Visualize the first 10 images of your dataset
​
Visualize your labels and save your custom dataset
/r/computervision
https://redd.it/10cet92
How good is the new YOLO? (YOLOv8)
A brief reviev of YOLOv8 capabilities, link is below: (No mailwall)
https://www.flyps.io/blog/a-new-yolo-is-here-yolov8
​
https://i.redd.it/zc8538mhgmba1.gif
/r/computervision
https://redd.it/10a0uvz
[OC] Distribution of 19 Types of Berries Native to North America + Approx. Berry Diversity/Density in NA
/r/dataisbeautiful
https://redd.it/106rh4r
When you can't afford a trip to the United States
/r/MapPorn
https://redd.it/1069x6y
[OC] Desktop Search Engine Global Market Share
/r/dataisbeautiful
https://redd.it/105zq9j
[OC] The cost of 6 months of Type 1 Diabetes
https://redd.it/105fbu6
@datascientology
[OC] Map showing temperature anomalies over the northern hemispher on New Year's Day
/r/dataisbeautiful
https://redd.it/105olnr
[OC] Metal bands bring happiness (as chocolate brings Nobel Prizes)
/r/dataisbeautiful
https://redd.it/105qgoi
Why are there more remote positions in the US than in EU
I am trying to get a remote position as a data scientist in EU but it seems like there are not many opportunities. Meanwhile when I change the location to the US there are about 100 times more position. I am wondering what the reason could be?
/r/datascience
https://redd.it/1052dli
How Stable was Each Country in 2022? (According to Fragile State Index)
/r/MapPorn
https://redd.it/105gi5x
"Everyone You Will Ever Meet Knows Something You Don't" - The 9 Types of Intelligence
/r/Infographics
https://redd.it/105h3kt