Do you lead or represent a free or open source software organization? Are you seeking new contributors? (Who isn’t?) Do you enjoy the challenge and reward of mentoring new developers? Apply to be a mentor organization for Google Summer of Code 2018!
We are seeking open source projects and organizations to participate in the 14th annual Google Summer of Code (GSoC). GSoC is a global program that gets student developers contributing to open source. Each student spends three months working on a project, with the support of volunteer mentors, for participating open source organizations.
Last year 1,318 students worked with 198 open source organizations. Organizations include individual projects and umbrella organizations that serve as fiscal sponsors, such as Apache Software Foundation or the Python Software Foundation.
You can apply starting today. The deadline to apply is January 23 at 16:00 UTC. Organizations chosen for GSoC 2018 will be posted on February 12.
It’s been an incredible (and incredibly busy!) three weeks for the 25 mentor organizations participating in Google Code-in (GCI) 2017, our seven week global contest designed to introduce teens to open source software development. Participants complete bite sized “tasks” in topics that include coding, documentation, UI/UX, quality assurance and more. Volunteer mentors from each open source project help participants along the way.
Total registered students has already surpassed 2016 numbers and we are less than halfway to the finish! We’re thrilled that high school students are embracing GCI like never before.
Check out some of the statistics below (current as of Thursday, December 14):
Total registered students: 6,146
Number of students who have completed at least one task: 1,573 (51% of those students have completed more than 3 tasks, earning them a GCI t-shirt)
Total number of tasks completed: 5,499
Most tasks completed by one student: 39
Top 5 Countries by Tasks Completed
Countries Represented by Mentors and Students
Of course, GCI wouldn’t be possible without the effort of the more than 725 mentors and organization administrators. Based in 65 countries, mentors answer questions, review submissions, and approve tasks for students at all hours of the day -- and sometimes night! They work tirelessly to help encourage and guide the next generation of open source contributors.
Every year we express our gratitude to the mentors and organization administrators. We are particularly grateful for them given how many more students are participating in GCI this year. Thank you all, and hang in there!
Training a neural network usually involves defining a loss function, which tells the network how close or far it is from its objective. For example, image classification networks are often given a loss function that penalizes them for giving wrong classifications; a network that mislabels a dog picture as a cat will get a high loss. However, not all problems have easily-defined loss functions, especially if they involve human perception, such as image compression or text-to-speech systems. Generative Adversarial Networks (GANs), a machine learning technique that has led to improvements in a wide range of applications including generating images from text, superresolution, and helping robots learn to grasp, offer a solution. However, GANs introduce new theoretical and software engineering challenges, and it can be difficult to keep up with the rapid pace of GAN research.
A video of a generator improving over time. It begins by producing random noise, and eventually learns to generate MNIST digits.
In order to make GANs easier to experiment with, we’ve open sourced TFGAN, a lightweight library designed to make it easy to train and evaluate GANs. It provides the infrastructure to easily train a GAN, provides well-tested loss and evaluation metrics, and gives easy-to-use examples that highlight the expressiveness and flexibility of TFGAN. We’ve also released a tutorial that includes a high-level API to quickly get a model trained on your data.
This demonstrates the effect of an adversarial loss on image compression. The top row shows image patches from the ImageNet dataset. The middle row shows the results of compressing and uncompressing an image through an image compression neural network trained on a traditional loss. The bottom row shows the results from a network trained with a traditional loss and an adversarial loss. The GAN-loss images are sharper and more detailed, even if they are less like the original.
TFGAN supports experiments in a few important ways. It provides simple function calls that cover the majority of GAN use-cases so you can get a model running on your data in just a few lines of code, but is built in a modular way to cover more exotic GAN designs as well. You can just use the modules you want -- loss, evaluation, features, training, etc. are all independent.. TFGAN’s lightweight design also means you can use it alongside other frameworks, or with native TensorFlow code. GAN models written using TFGAN will easily benefit from future infrastructure improvements, and you can select from a large number of already-implemented losses and features without having to rewrite your own. Lastly, the code is well-tested, so you don’t have to worry about numerical or statistical mistakes that are easily made with GAN libraries.
Most neural text-to-speech (TTS) systems produce over-smoothed spectrograms. When applied to the Tacotron TTS system, a GAN can recreate some of the realistic-texture, which reduces artifacts in the resulting audio.
When you use TFGAN, you’ll be using the same infrastructure that many Google researchers use, and you’ll have access to the cutting-edge improvements that we develop with the library. Anyone can contribute to the github repositories, which we hope will facilitate code-sharing among ML researchers and users.
By Joel Shor, Senior Software Engineer, Machine Perception
