Posts on May 2017

Drawing app “pixiv Sketch” and automatic coloring service “PaintsChainer” collaborate to provide a new function for automatic coloring of illustrations!

Artificial Intelligence (AI) supports the “coloring” of sketches and illustrations by providing new functions to recognize faces, clothes, and background in the image and automatically filling them with color and shading.

Tokyo, Japan, 24 May 2017 – pixiv Inc. (President: Hiroki Ito, Headquarters: Shibuya-ku, Tokyo) and AI startup Preferred Networks, Inc.  (President & CEO: Toru Nishikawa, Headquarters: Chiyoda-ku, Tokyo, hereinafter referred to as PFN) collaborate to add the new function of automatic coloring, realized by “PaintsChainer”, to the drawing communication platform “pixivSketch”, available from Wednesday, May 24, 2017.

pixiv Sketch is a communication platform that allows users to post drawings directly from devices such as PCs and smartphones. Even when relaxing or playing outside with friends, users can paint anytime and anywhere and experience communication in real-time by posting and sharing their drawings.

The new functionality added to pixiv Sketch is realized using the technology of PaintsChainer that can automatically select painting colors, trained from pairs of line drawings and colored illustrations using Chainer, a deep learning framework developed and provided by PFN.

It allows the user to perform the important process of “coloring” when producing illustrations by selecting a picture drawn on pixiv Sketch or an external image file and then simply clicking the automatic coloring button. Face, clothing, and the background of the illustration are recognized by AI and colors are automatically added. You can also put your favorite color chosen from a color palette as a hint for automatic coloring at any point on the line drawing.

pixiv and PFN will continue to provide valuable services to make drawing and painting more natural and pleasant through AI technology and research.

◆ Automatic coloring function in pixiv Sketch

Release date: May 24th

Cost: free

URL: https://sketch.pixiv.net/ (Available only on Web version)

How to use the new function;

1. Draw a line drawing or select an image of a line drawing

2. Start the automatic coloring tool by pressing the “Automatic coloring” button

3. Select the coloring pattern of your choice from two different styles

4. If necessary, put color hints from the color palette to adjust the coloring

5. After specifying the colors, click the arrow button to complete the coloring process!

 

◆ pixiv Sketch  https://sketch.pixiv.net/

pixiv Sketch is a painting communication platform launched with the desire to “make everyday paintings more casual and fun”. It is a service where you can post images you’ve painted anytime, anywhere through devices such as PCs and smartphones.

 

PaintsChainer   https://paintschainer.preferred.tech/

PaintsChainer is developed and offered by PFN, and received had a great response on Twitter and other social media sites when the service was released in January 2017. Users can upload a black and white drawing file and have it colored automatically using deep learning technology. The user can also supply color hints to control the colorization results.

 

◆ About Preferred Networks, Inc. https://www.preferred-networks.jp/

Founded in March 2014 with the aim of business utilization of deep learning technology focused on IoT. PFN advocates Edge Heavy Computing as a way to handle the enormous amount of data generated by devices in a distributed and collaborative manner at the edge of the network and realizes innovation in the three priority business areas of the transportation system, manufacturing industry, and bio/healthcare.

PFN develops and provides solutions based on the Deep Intelligence in Motion (DIMo) platform that provides state-of-the-art deep learning technology. PFN promotes advanced initiatives by collaborating with world leading organizations, such as Toyota Motor Corporation, Fanuc Inc., National Cancer Research Center.

Preferred Networks and Microsoft have a strategic collaboration in the field of deep learning solutions

Tokyo, Japan, 23 May 2017 – Today, Preferred Networks, Inc. (Headquarters: Chiyoda-ku, Tokyo, President and CEO: Toru Nishikawa, hereinafter referred to as PFN) and Microsoft Corporation (Headquarters: Redmond, Washington, USA, CEO: Satya Nadella) have agreed to strategically collaborate in the field of deep learning solutions with the aim to accelerate the applications of artificial intelligence and deep learning in the business world.

Based on this alliance, both companies will promote cooperation between Microsoft’s public cloud platform Microsoft Azure and PFN’s deep learning technology to provide deep learning solutions for solving problems across a broad range of industries. Microsoft Corporation (Headquarters: Minato-ku, Tokyo, Representative Director: Takuya Hirano) will fully support the delivery of this collaboration to the Japanese market.

 

Through this collaboration, both companies will work together in the following three areas;
1) Technology, 2) Human resource development, 3) Marketing.

1.Technology:

  • Challenges that engineers face in deep learning include the increase of the time required to train complex neural networks, the growing management complexity associated with ever-increasing data, to remain flexible and adaptable to the rapid progress and innovation of deep learning, and the methodology of system development around deep learning. In this collaboration, with the aim of tackling these challenges, both companies will enhance the compatibility between Microsoft Azure IaaS and PFN’s deep learning framework Chainer, providing an Azure template to deploy Chainer/ChainerMN (MN stands for Multi Node) on Azure IaaS with a single click, Chainer to Data Science VMS, Chainer on Azure batch services and SQL Servers, and improving Chainer on Windows by the summer of 2017.

 

  • Currently, the standard way of developing neural networks is to develop from scratch. However, it needs high technical knowledge, and the amount of required investment is also very large. In order to drive the application of deep learning to the real world, it is essential to move from development from scratch to standardized solutions. To realize this transfer, Microsoft Azure Data + Analytics products and PFN’s deep learning platform, Deep Intelligence in-Motion (DIMo) are combined to provide solutions for specific workloads and industries throughout 2017. In addition, both companies will support and nurture partnerships in the development of these solutions to accelerate the broader implementation in the real world.

 

2.Human resource development:

  • The development of data science human resources is one of the main issues of applying deep learning to the real world. In order to resolve this issue, both companies will work together to provide training programs for university students, engineers and researchers throughout 2017. In addition, both companies will consider participation in data science related programs for human resource development, which are typically government organized, for higher education institutions.

 

  • Training programs include not only the basics of neural networks, but also advanced classes that teach how to actually apply deep learning to real-world business applications. Through these training programs, both companies plan to train 50,000 people in three years. As goals for the training, programs such as Imagine Cup and Azure for Research, which are among the world’s largest IT contests for students aiming for fostering international competitive IT talents are considered.

 

3.Marketing:

  • Deep learning is just one method in machine learning, but it is now exposed to many people as a related field of artificial intelligence. As a result, it is difficult for customers to determine whether or not deep learning is effective to solve their business problems. Both companies will start a customer workshop for each industry in the summer of 2017 based on the knowledge of deep learning business cultivated by Microsoft and PFN, and real success stories using Microsoft Azure, Chainer and DIMo.

 

  • By incorporating the latest deep learning technologies provided by Chainer and DIMo on a solid Azure platform, both companies provide an enterprise-grade end-to-end solution that can be incorporated into the customer’s core system, throughout 2017.

 

  • As a place of matching between customers who want to solve business problems with deep learning, and companies who provide consulting services and system development using deep learning, a community named “Deep Learning Lab” has been established, and the community will hold briefings on the days of June 19 and July 25, 2017.
    https://dllab.connpass.com/

Preferred Networks released ChainerMN, a multi-node extension to Chainer, an open source framework for deep learning

Tokyo, Japan, 9 May 2017 –

Today, Preferred Networks, Inc. (Headquarters: Chiyoda-ku, Tokyo, President and CEO: Toru Nishikawa, hereinafter PFN)  released ChainerMN (MN stands for Multi-Node, https://github.com/pfnet/chainermn), which can accelerate the training speed by adding a distributed learning function with multiple GPUs to Chainer, the open source deep learning framework developed by PFN.

Even though the performance of GPUs is continuously improving, the ever-increasing complexity of neural network models, with large number of parameters and much larger training datasets requires more and more computational power to train these models. Today, it is common that one training session takes more than a week on a single node of a state-of-the-art computer.

Aiming to provide researchers with an efficient way to conduct flexible trial and error iterations, while using large training data sets PFN developed ChainerMN, a multi-node extension for high-performance distributed training, built on top of Chainer. We demonstrated that ChainerMN finished training a model in about 4.4 hours with 32 nodes and 128 GPUs which would require about 20 days on a single-node, single GPU machine.

 

  • Performance comparison experiment between ChainerMN and other frameworks

https://research.preferred.jp/2017/02/chainermn-benchmark-results/

We compared the performance benchmark result of ChainerMN with those of other popular multi-node frameworks. In our 128-node experiments with a practical setting, in which the accuracy is not sacrificed too much for speed, ChainerMN outperformed other frameworks.

 

When comparing the scalability, although the single-GPU throughputs of MXNet and CNTK (both are written in C++) are higher than ChainerMN (written in Python), we found that the throughput of ChainerMN was the highest with 128 GPUs, showing that ChainerMN is the most scalable. This result was due to the design of ChainerMN which is optimized for both intra-node and inter-node communications.

 

Existing Chainer users can easily benefit from the performance and scalability of ChainerMN simply by changing a few lines of their original training code.

ChainerMN has already been used in multiple projects in a variety of fields such as natural language processing and reinforcement learning.

 

  • About the open source deep learning framework Chainer

Chainer is a Python-based deep learning framework developed by PFN, which has unique features and powerful performance that enables users to easily and intuitively design complex neural networks thanks to its “Define-by-Run” feature. Since it was open-sourced in June 2015, as one of the most popular frameworks, Chainer has attracted not only the academic community but also many industrial users who need a flexible framework to harness the power of deep learning in their research and real-world applications. (http://chainer.org/

 

  • About Preferred Networks, Inc.

Founded in March 2014 with the aim of business utilization of deep learning technology focused on IoT. Edge Heavy Computing handles the enormous amount of data generated by devices in a distributed and collaborative manner at the edge of the network and realizes innovation in the three priority business areas of the transportation system, manufacturing industry, and bio- healthcare.

PFN develops and provides solutions based on the Deep Intelligence in-Motion (DIMo, Daimo) platform that provides state-of-the-art deep learning technology. Collaborating with world leading organizations, such as Toyota Motor Corporation, Fanuc Inc., National Cancer Research Center,  we are promoting advanced initiatives.(https://www.preferred-networks.jp/en/

 

*Chainer(R) and DIMo(TM) are a trademark of Preferred Networks, Inc. in Japan and other countries.