Deep Learning Weekly Issue #124
Structured learning and GANs in TF, another viral face-swapper, optimizer benchmarks, and more...
|Sep 4, 2019|
This week in deep learning we bring you a GAN library for TensorFlow 2.0, another viral face-swapping app, an AI Mahjong player from Microsoft, and surprising results showing random architecture search beating neural architecture search.
You may also enjoy an interview with Yann LeCun on the AI Podcast, a primer on MLIR from Google, a few-shot face-swapping GAN, benchmarks for recent optimizers, a structured learning framework for TensorFlow, and more!
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 TensorFlow team has released some handy code for training GANs with TF 2.0.
A new TensorFlow framework for incorporated structured data into training.
Zao, a new deepfake face swapping app, hit Chinese app stores last week and found itself on a similar viral trajectory to FaceApp.
Microsoft takes on Mahjong players and wins. Another game “defeated” by AI.
Lex Fridman interviews Facebook’s Yann LeCun on the AI Podcast.
A deeper dive into neural networks without trained weights (sorta).
A three part tutorial covering graph neural network approaches to CV.
A great read from Google on intermediate representations and why they'll be increasingly important for deep learning.
A summary of popular speech synthesis methods.
Libraries & Code
Generative adversarial networks integrating modules from FUNIT and SPADE for few-shot face-swapping.
A helpful collection of benchmarks for new optimizers (spoiler: RangerLars wins).
Facebook open sources a tool to automatically tune their fastText classifier.
Change notes for the latest TF 2.0 release candidate.
Papers & Publications
Abstract: Neural Architecture Search (NAS) aims to facilitate the design of deep networks for new tasks. Existing techniques rely on two stages: searching over the architecture space and validating the best architecture. NAS algorithms are currently evaluated solely by comparing their results on the downstream task. While intuitive, this fails to explicitly evaluate the effectiveness of their search strategies. In this paper, we present a NAS evaluation framework that includes the search phase. To this end, we compare the quality of the solutions obtained by NAS search policies with that of random architecture selection. We find that: (i) On average, the random policy outperforms state-of-the-art NAS algorithms; (ii) The results and candidate rankings of NAS algorithms do not reflect the true performance of the candidate architectures; and (iii) The widely used weight sharing strategy negatively impacts the training of good architectures, thus reducing the effectiveness of the search process. We believe that following our evaluation framework will be key to designing NAS strategies that truly discover superior architectures.