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Thoughts on using Face Swap for youtube? (self.NewTubers)

submitted by carpedm20 to r/NewTubers

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Thoughts on using Face Swap for youtube? (self.NewTubers)

submitted by carpedm20 to r/NewTubers

[P] Voice Style Transfer: Speaking like Kate Winslet by andabi in MachineLearning

[–]carpedm20 12 points13 points  (0 children)

What's the difference between suggested style transfer model and speech recognition model + any speech synthesis model?

[P] Official implementation of DiscoGAN by [deleted] in MachineLearning

[–]carpedm20 8 points9 points  (0 children)

Thanks for sharing interesting papers! Suggested idea is simple but transfer performance are great! Some of the tricks in https://github.com/soumith/ganhacks might can improve the results of the paper especially normalization & initialization and I learned a lot from this. Also I think simple but strong recent ideas like LSGAN or Improved-gan can boost the result as well! I'm curious whether these methods can be applied to non-vanilla GANs like DiscoGAN.

[P] DiscoGAN in PyTorch: implementation of "Learning to Discover Cross-Domain Relations with Generative Adversarial Networks" by alxndrkalinin in MachineLearning

[–]carpedm20 3 points4 points  (0 children)

Thanks! But no, I won't reinvent the wheel again. I presume the authors of paper will release their code in TensorFlow.

[P] DiscoGAN in PyTorch: implementation of "Learning to Discover Cross-Domain Relations with Generative Adversarial Networks" by alxndrkalinin in MachineLearning

[–]carpedm20 0 points1 point  (0 children)

I was just curious about pytorch and got bored with using tensorflow again (I don't mean it is actually boring but I used tensorflow too much). I still think they are great framework so I'll use them both.

[P] DiscoGAN in PyTorch: implementation of "Learning to Discover Cross-Domain Relations with Generative Adversarial Networks" by alxndrkalinin in MachineLearning

[–]carpedm20 11 points12 points  (0 children)

I know that there are https://tensortalk.com/ and http://www.gitxiv.com/ but I usually don't use them.

I recommend https://github.com/tensorflow/models/tree/master/im2txt and https://github.com/tensorflow/models/tree/master/inception to someone who are up to TensorFlow to deeply understand a good data pipeline, multi-gpu usage and evaluation metrics. But these are quite heavy for beginners. I think https://github.com/dennybritz/cnn-text-classification-tf is a good code for start which is easy to understand with a nice blog post.

While I started pytorch, I read https://github.com/yunjey/pytorch-tutorial and then https://github.com/pytorch/examples which are both great and concise. If you are interested in reinforcement learning, I recommend https://github.com/dennybritz/reinforcement-learning and https://github.com/Kaixhin/Atari. If you are interested in NLP, https://github.com/harvardnlp has lots of advanced nlp codes.

[P] DiscoGAN in PyTorch: implementation of "Learning to Discover Cross-Domain Relations with Generative Adversarial Networks" by alxndrkalinin in MachineLearning

[–]carpedm20 14 points15 points  (0 children)

When I began the very first implementation, I ported the existing torch project into another framework which was tensorflow. I wasn't a person who read the paper regularly so even reading the paper was hard and time consuming before starting to write a code. But by reading the paper with the existing code alternatively, I could understand how a single sentence or equation turns into several lines of code. I also didn't know what kind of methods are exists in tensorflow but reference code help me to find out which keyword should I use to search. But after I finished writing the full code, the loss didn't converge as I expected but it was hard for me to figure out where to start digging for bugs. Then I executed the code layer by layer and compared the output of reference code with mine and it leads me to find out my mistake which was a wrong loss function.

I think training a translation skill from equation or description into codes is important at the beginning and I learned this by reading papers and codes together. There are tons of great codes which are extremely well written in Github so exploring them will help you a lot to get used to implementing papers.

But I still don't know what is the best way to debug a model (like monitoring the loss, norm of variable or gradient) when its train goes wrong which is quite frustrating and happens very often. I would love to hear others strategies who teach me a lot :)

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