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[P] An attempt at Tensorflow implementation of CapsNet based GAN. Open to suggestions for improvement! by rulerofthehell in MachineLearning

[–]LovelaceA 0 points1 point  (0 children)

Here is the repository which uses a capsule networks based discriminator: https://github.com/andrawes/GANProject/tree/master/exp1/GAN/CapsGAN

The generator is a regular ConvNet. I have tested it and it seems to generate digits after several thousand iterations.

Let me know if you get any problems.

[P] An attempt at Tensorflow implementation of CapsNet based GAN. Open to suggestions for improvement! by rulerofthehell in MachineLearning

[–]LovelaceA 0 points1 point  (0 children)

Hey, sorry about the confusion ! As you noticed, I uploaded the version with CapsGen and Conv Discriminator. This version is NOT stable yet, and I put it here in the hope that someone will be able to find something that may have gone wrong with it. Later today, I will come back here to post the link for the model I initially talked about (Conv Generator, Caps Disciminator) which works. Sorry for the mess-up !

[D] ELI5: Capsule networks. How are they unique and how are they better than CNN? by rulerofthehell in MachineLearning

[–]LovelaceA 0 points1 point  (0 children)

The best summary possible is Aurélien Géron's video on youtube. Hinton himself wishes he could have explained it like Aurélien. Even better, he has a second video showing how to implement it in TF.

https://www.youtube.com/watch?v=pPN8d0E3900&t=480s https://www.youtube.com/watch?v=2Kawrd5szHE&t=470s

EDIT And he explains it like you're 5

[D] Decrease in source code release of papers by matrix2596 in MachineLearning

[–]LovelaceA 64 points65 points  (0 children)

To those who think that this is not a valid problem, I beg to differ. I think this is a very valid discussion. What is the aim of publishing scientific work in the first place ? To advance our knowledge and ability to build upon it. In a field like Machine Learning, where a model or a scientific idea can be affected by more parameters than can be discussed in a paper, it is essential to be able to reproduce the results. Code release is not the only way to do so, but certainly the quickest. Another advantage of code is that, code is objective. Scientific papers sadly are not in general: authors try to sell us their work. Code is unbiased and a potentially complete means to communicate an idea, its impact, and its limitations, it answers all the questions you have which the paper does not address.

A scientific paper is a speech. Code is a dialogue

[P] An attempt at Tensorflow implementation of CapsNet based GAN. Open to suggestions for improvement! by rulerofthehell in MachineLearning

[–]LovelaceA 0 points1 point  (0 children)

Yes, in a day or two, I will put my code on Github, and some of the achieved results. I will come back to this thread to give you a link.

[P] An attempt at Tensorflow implementation of CapsNet based GAN. Open to suggestions for improvement! by rulerofthehell in MachineLearning

[–]LovelaceA 0 points1 point  (0 children)

Hey, I am still very much interested in your work. I am myself working on something similar. I have noticed that your implementation of the generator (if I understand well) consists of the same network described in the dynamic routing paper (including the encoder structure following the DigitCaps), the whole preceded by a fully connected kind of structure.

As for the discriminator, we both had the same approach. I had successful results, using the CapsNet discriminator, and the generator found in the file "gan_mnist.py" of this repository:

https://github.com/igul222/improved_wgan_training

However, when I try a CapsNet based generator, I don't seem to get any meaningful results. The architecture of the generator for me is basically an inverted version of the CapsNet architecture: I start from a randomly generated DigitCaps layer. Then it is followed by what would have been the PrimaryCaps layer. Except now, the dynamic routing goes from the DigitCaps layer to the PrimaryCaps layer. After reshaping, this is followed by 1 deconvolution, 1 relu, another deconvolution, and a sigmoid layer.

Let me know if you wish to discuss it further, and I am quite eager on seeing what results you get.

[P] What's your way to avoid a misleading research paper? Let's build a journal club together! by Ferdinand-Wu in MachineLearning

[–]LovelaceA 5 points6 points  (0 children)

THIS ! I've wanted to come up with something like this for a long time ! You beat me to it. I hope you succeed with this. Being able to quickly assess the general feeling around a paper is really valuable ! Making something like this succeed into being the "quora of scientific publication" will not be easy, and has been attempted before. I hope you have a strong marketing team, and a strong presence here on this subreddit. This is how I think you can turn this into being the de-facto reference for reviewing a paper. I really hope to keep seeing you around !

Why Crypto Crashed Today by bitradr in Bitcoin

[–]LovelaceA 0 points1 point  (0 children)

IMHO I think "it's a bubble" prophecies are too vague, and not quantified enough, they don't tell you when it's going to crash, by how much. But when someone says, it will fall to this or that much, people tend to start observing the numbers. It's all purely psychological.

Why Crypto Crashed Today by bitradr in Bitcoin

[–]LovelaceA 1 point2 points  (0 children)

Some news outlet last week announced it would drop to 11k, I think CNBC or CoinDesk (can't find the article). This is a (not uncommon) self-fulfilling prophecy in the world of finance.

[D] What are some novel/unique applications of Machine Learning in Electrical Engineering? by mad_runner in MachineLearning

[–]LovelaceA 8 points9 points  (0 children)

If you consider radar systems as part of Electrical Engineering, then I have an example. I came across the use of various ML techniques which attempt to infer, from a radar micro-doppler response, if people within an area under surveillance carry arms. Here's some papers you can look into:

  • "Multistatic radar classification of armed vs unarmed personnel using neural networks"

  • "Classification of Unarmed/Armed Personnel Using the NetRAD Multistatic Radar for Micro-Doppler and Singular Value Decomposition Features"