Deep-Learning-Papers-Reading-Roadmap
Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech! (by floodsung)
faceswap
Deepfakes Software For All (by deepfakes)
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Deep-Learning-Papers-Reading-Roadmap | faceswap | |
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5 | 10 | |
37,120 | 49,112 | |
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0.0 | 8.0 | |
over 1 year ago | 4 days ago | |
Python | Python | |
- | GNU General Public License v3.0 only |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Deep-Learning-Papers-Reading-Roadmap
Posts with mentions or reviews of Deep-Learning-Papers-Reading-Roadmap.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-09-08.
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[D] Resources for Understanding The Original Transformer Paper
https://github.com/floodsung/Deep-Learning-Papers-Reading-Roadmap - This one is a bit dated so it doesn’t contain all of the papers that you need to read to get up to date but I think you should definitely read all of the papers in this list and implement as much as you can.
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4 ML Roadmaps to Help You Find Useful Resources to Learn From
Deep Learning Papers Reading Roadmap
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Should I implement every famous DL paper? [D]
I found a really great list of introductory and popular dl papers (github.com/floodsung/Deep-Learning-Papers-Reading-Roadmap) and I would absolutely implement every paper on this list if I had the time (at least a mini version e.g. CIFAR10 instead of ImageNet). Is is essential for me to implement every single paper on that list to become a good DL researcher and to start reading/implementing more recent ones? All the papers on the list are from before 2017 and I can't wait to start exploring the latest research! Would I be able to get away with just implementing a handful of papers from that list?
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[D] How did you implement papers with models that required a lot of GPUs to train?
I'm self-learning ML and trying to implement the papers listed here but I don't have access to hundreds of free GPUs like those corpos do.
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Looking for Beginner CV Resources
Definitely check out this list https://github.com/floodsung/Deep-Learning-Papers-Reading-Roadmap It's all papers, you should get used to reading scientific material.
faceswap
Posts with mentions or reviews of faceswap.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2024-01-30.
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faceswap VS facefusion - a user suggested alternative
2 projects | 30 Jan 2024
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A beginner guide into deepfakes
Head over to deepfakes/faceswap and install all the stuff that it asks you to do and then open the terminal with in faceswap env from anaconda.
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[D] How is it checked if models do not just memorize their training examples?
But there's a nice survey on Arxiv here of various deepfake / face swap methods. Some of methods listed in the table on page 4, such as Faceswap and Faceswap-GAN, apparently use encoder-decoder models. I think Faceswap-GAN was the one that I was thinking of in particular; apparently it adds a perceptual loss and an adversarial loss to an autoencoder.
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Use the infamous Deep Fakes project for things other than faces
My current challenge is getting those masked wheel images to be able to swap between images, or to apply a new wheel on a car image. To get a decent result that doesn't look fake, it would have to do some minor warping and resizing. To me, this seems like exactly what the Deep Fakes repo does. https://github.com/deepfakes/faceswap
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Is it just me, or is faceswap installation trolling me?
It keeps getting stuck at either"fatal: unable to access 'https://github.com/deepfakes/faceswap.git/':" or "Please run this script with Python version 3.7 or 3.8 64bit and try again."
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Emma Watson
https://github.com/deepfakes/faceswap bu uygulamadan yaparsın
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Whole Dutch parliamentary of foreign affairs fooled by a deepfake zoom call of an employee of Alexei Navalny
Here's the faceswap github
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vee pee enn
Deepfaking as it is right now, is mostly two types of software, and two very different methods. The first is making a video with a "face swap". Those use software called Deepfacelab or Faceswap. I personally like Deepfacelab because it's more "configurable" at least how I see it. This is what you see on youtube or what makes headlines when the media decides to show clips of the dangerous political deepfakes.
- Is there a free easy-to-use program to make deepfakes?
- The progress of deepfake- thoughts?
What are some alternatives?
When comparing Deep-Learning-Papers-Reading-Roadmap and faceswap you can also consider the following projects:
Real-Time-Voice-Cloning - Clone a voice in 5 seconds to generate arbitrary speech in real-time
DeepFaceLab - DeepFaceLab is the leading software for creating deepfakes.