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Facebook's machine learning system MelNet generated this AI voice clone of Bill Gates. Others voice clones can be heard here: https://audio-samples.github.io under the heading “Selected Speakers.”
Are you really into object recognition, but you are sick of looking at 2D boxes and 2D masks? Let's play with 3D shapes!
We build on Mask R-CNN and extend it to infer 3D meshes. Given an input image, we detect all objects, infer their 2D instance boxes and masks as well as their 3D object shapes, all end-to-end! Naturally, we call our approach Mesh R-CNN :D

Paper: https://arxiv.org/abs/1906.02739
Joint work with Jitendra Malik and Justin Johnson

P.S. This task is really hard!
Here are the COMPLETE Lecture notes on Professor Andrew Ng's
Stanford Machine Learning Lecture: https://www.holehouse.org/mlclass/
What is the fuss about TensorFuzz?
It is the fun automated software “testing” for neural networks,
adapting traditional coverage guided fuzzing techniques.
Run #TensorFuzz to take your test coverage to levels other methods cannot reach (e.g. activation coverage, not just class coverage)

Great work by Augustus Odena and @Ian Goodfellow
Join the #ICML2019 talk at 9:40am today. Grand Ballroom

Read at https://arxiv.org/pdf/1807.10875.pdf
Code: https://github.com/brain-research/tensorfuzz
Shapes and Context:
In-the-wild Image Synthesis & Manipulation

https://www.cs.cmu.edu/~aayushb/OpenShapes/
Adaptive Nonparametric Variational Autoencoder. arxiv.org/abs/1906.03288