Style-based GAN architecture produces impressive image generation results, but it’s not without its limitations. NVidia’s research team has been hard at work fixing some of the problems with StyleGAN (artifacts).
In addition to improving image quality, this path length regularizer yields the additional benefit that the generator becomes significantly easier to invert. This makes it possible to reliably detect if an image is generated by a particular network.
Folks have been talking about TensorFlow 2 for some time now (See Practical AI #42 for one excellent example), but now it’s finally here. The bulleted list:
- Easy model building with Keras and eager execution.
- Robust model deployment in production on any platform.
- Powerful experimentation for research.
- API simplification by reducing duplication and removing deprecated endpoints.
This is a huge release. Check out the highlights list in the changelog to see for yourself.
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The strong advantage of TensorFlow is it flexibility in designing highly modular models which can also be a disadvantage for beginners since a lot of the pieces must be considered together when creating the model.
If you’re interested in TensorFlow, but haven’t dove in yet for one reason or another, this might be a good place to start.
Abhishek Singh isn’t deaf or mute, but that didn’t stop him from asking the question:
If voice is the future of computing interfaces, what about those who cannot hear or speak?
A reimplementation of TensorFlow for Ruby. This is a ground up implementation with no dependency on TensorFlow. Effort has been made to make the programming style as near to TensorFlow as possible, comes with a pure Ruby evaluator by default with support for an opencl evaluator for large models and datasets.
a pure Python implementation of a neural-network based Go AI, using TensorFlow
This is not trying to be the top Go AI program. They want to build a readable, understandable implementation that can benefit the community.