|March 27 · Issue #102 · View online |
As always, happy reading and hacking. If you enjoy this newsletter, please recommend us to your friends and colleagues.
Until next week!
| AI-powered Google Doodle celebrates Johann Sebastian Bach’s birthday - Vox |
Google released its first ever AI-powered doodle. The Magenta and PAIR teams worked together to train a small convolutional neural network to create musical score in the style of Bach based on user input. Everything runs in the browser with a Tensorflow.js model weighing just a few hundred kilobytes.
| What can machine learning tell us about the solid Earth? |
Deep learning meets geology. Scientists are training machine learning algorithms to help shed light on earthquake hazards, volcanic eruptions, groundwater flow and longstanding mysteries about what goes on beneath the Earth’s surface.
| Do you see what AI sees? Study finds that humans can think like computers |
Adversarial examples are specifically designed to fool neural networks. Researchers showed those images to humans and found that humans were likely to guess which mistakes the networks would make.
| Liveness Detection with OpenCV - PyImageSearch |
Learn how to detect liveness with OpenCV, Deep Learning, and Keras. You’ll learn how to detect fake faces and perform anti-face spoofing in face recognition systems with OpenCV.
| Remastering Star Trek: Deep Space Nine with Machine Learning |
Applying deep learning-based super resolution techniques to upscale Star Trek: Deep Space Nine to 4k.
| Google AI Blog: Reducing the Need for Labeled Data in Generative Adversarial Networks |
A new data augmentation technique from Google that reduces the amount of labeled data needed to train GANs by a factor of 10.
| A Simple Guide to Semantic Segmentation – BeyondMinds – Medium |
A comprehensive review of methods and loss functions for Semantic Segmentation using Deep Learning and Classical methods, and an introduction to their applications.
| GitHub - xgarcia238/8bit-VAE: An implementation of MusicVAE made for the NES MDB in PyTorch. |
Synthesizing chip tunes with VAEs in PyTorch.
| GitHub - ajbrock/BigGAN-PyTorch: The author's officially unofficial PyTorch BigGAN implementation. |
The author’s officially unofficial PyTorch BigGAN implementation. - ajbrock/BigGAN-PyTorch
| GitHub - shevisjohnson/gpt-2_bot: This is a reddit bot based on OpenAI's GPT-2 117M model |
This is a reddit bot based on OpenAI’s GPT-2 117M model - shevisjohnson/gpt-2_bot
| GitHub - TrebleStick/GravityRush: Gravity Rush - Mobile game for muscle rehabilitaion |
Gravity Rush - Mobile game for muscle rehabilitaion - TrebleStick/GravityRush
| Dataset list — A list of the biggest datasets for machine learning from across the web |
A list of the biggest datasets for machine learning from across the web.
| r/MachineLearning - [P] Dataset: 480,000 Rotten Tomatoes reviews for NLP. Labeled as fresh/rotten |
240,000 fresh reviews and 240,000 rotten reviews, labeled, with their text review from critics.
| Papers With Code : Corners for Layout: End-to-End Layout Recovery from 360 Images |
Abstract: The problem of 3D layout recovery in indoor scenes has been a core research topic for over a decade. However, there are still several major challenges that remain unsolved. Among the most relevant ones, a major part of the state-of-the-art methods make implicit or explicit assumptions on the scenes – e.g. box-shaped or Manhattan layouts.
| Papers With Code : Photorealistic Style Transfer via Wavelet Transforms |
Abstract: Recent style transfer models have provided promising artistic results. However, given a photograph as a reference style, existing methods are limited by spatial distortions or unrealistic artifacts, which should not happen in real photographs. We introduce a theoretically sound correction to the network architecture that remarkably enhances photorealism and faithfully transfers the style.