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Deep Learning Weekly Issue #120
Scale becomes a unicorn, an AI learns to knit, an object counting API, listening to models train and more...
This week in deep learning we bring you news of a new AI unicorn, a model to detect kidney injury 48 hours in advance, an AI that can knit, and an update to ERNIE, an NLP model from Baidu.
You may also enjoy an overview of StyleGANs, a call for comments on the TensorFlow model garden redesign, a project that turns neural network training into sound, a pose estimation model and app for Android, and an open source framework for counting objects in images and video.
As always, happy reading and hacking. If you have something you think should be in next week's issue, find us on Twitter: @dl_weekly.
Until next week!
The data labeling and curation startup has seen impressive growth since its founding in 2016.
DeepMind trains a model on electronic medical records to predict acute kidney injury 48 hours before it occurs.
At least one user has received a pop-up suggesting Google is going to test monetization for Colab notebooks in the near future.
The TensorFlow team has released a number of requests for comments on everything from on-device training to the model garden redesign.
Researchers at MIT train a deep learning model to transform 2D knitting instructions into machine-readable actions.
Google’s PoseNet model gets a corresponding app along with a parameter update to improve accuracy.
Using loss values and gradients to generate sound waves so that you can listen to models as they train.
What we’d like to find out about GANs that we don’t know yet.
A nice overview of StyleGANs.
Libraries & Code
The TensorFlow Object Counting API is an open source framework built on top of TensorFlow and Keras that makes it easy to develop object counting systems.
The code behind the recent weight-agnostic neural networks has been released.
Markerless pose estimation of user-defined features with deep learning for all animals, including humans.
Papers & Publications
Abstract: We propose a novel method for unsupervised image-to-image translation, which incorporates a new attention module and a new learnable normalization function in an end-to-end manner. The attention module guides our model to focus on more important regions distinguishing between source and target domains based on the attention map obtained by the auxiliary classifier. Unlike previous attention-based methods which cannot handle the geometric changes between domains, our model can translate both images requiring holistic changes and images requiring large shape changes. Moreover, our new AdaLIN (Adaptive Layer-Instance Normalization) function helps our attention-guided model to flexibly control the amount of change in shape and texture by learned parameters depending on datasets….
Abstract: ...In order to extract to the fullest extent, the lexical, syntactic and semantic information from training corpora, we propose a continual pre-training framework named ERNIE 2.0 which builds and learns incrementally pre-training tasks through constant multi-task learning. Experimental results demonstrate that ERNIE 2.0 outperforms BERT and XLNet on 16 tasks including English tasks on GLUE benchmarks and several common tasks in Chinese.