Domain Translation is the problem of finding a meaningful correspondence between two domains. Since in a majority of settings paired supervision is not available, much work focuses on Unsupervised Domain Translation (UDT) where data samples from each domain are unpaired. Following the seminal work of CycleGAN for UDT, many variants and extensions of this model have been proposed. However, there is still little theoretical understanding behind their success. We observe that these methods yield solutions which are approximately minimal w.r.t. a given transportation cost, leading us to reformulate the problem in the Optimal Transport (OT) framework. This viewpoint gives us a new perspective on Unsupervised Domain Translation and allows us to prove the existence and uniqueness of the retrieved mapping, given a large family of transport costs. We then propose a novel framework to efficiently compute optimal mappings in a dynamical setting. We show that it generalizes previous methods and enables a more explicit control over the computed optimal mapping. It also provides smooth interpolations between the two domains. Experiments on toy and real world datasets illustrate the behavior of our method.
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The Vector Institute is pleased to announce that the 2019-20 Vector Scholarship in AI will be awarded to 78 meritorious nominees from 11 Ontario universities enrolling in 32 master’s programs in core-technical and complementary AI-related fields such as business and health care. This is the second round of scholarships announced by the Vector Institute after having launched the program with 66 scholarship recipients in 2018-19.
The Vector Scholarship in Artificial Intelligence, together with internships and networking programs, is a core component of the Vector Institute’s RAISE initiative, supported by the Province of Ontario, to develop and connect Ontario’s AI workforce to fuel AI-based economic development and job creation.
Through the RAISE initiative, the Vector Institute continues to work with universities to enhance existing master’s programs and create new master’s programs resulting in an increase in the number of prospective students applying, enrolling and ultimately entering the workforce with the AI skills and competencies sought by industry. The Vector Scholarship in AI recognizes promising AI talent and supports recruitment of top students to AI-related master’s programs in Ontario.
By 2021 it is expected that 700+ AI master’s students will have graduated and entered the Ontario workforce. These new graduates will meet ever-increasing demand from leading global companies and promising AI startups locating in Ontario in order to access the growing AI workforce.
Vector will launch the 2020-21 scholarship competition on January 6, 2020. Candidates who wish to be considered for a Vector Scholarship in Artificial Intelligence can find eligibility and nomination details here: vectorinstitute.ai/aimasters.
About the Vector Institute
The Vector Institute is an independent, not-for-profit corporation dedicated to advancing artificial intelligence, excelling in machine and deep learning. The Vector Institute’s vision is to drive excellence and leadership in Canada’s knowledge, creation, and use of AI to foster economic growth and improve the lives of Canadians. The Vector Institute is funded by the Province of Ontario, the Government of Canada through the Pan-Canadian AI Strategy administered by the Canadian Institute for Advanced Research (CIFAR), and industry sponsors from across the Canadian economy.
Learn More:
https://vectorinstitute.ai/AImasters/
*Scholarships are conditional upon students registering full-time in their proposed master’s program of study.
For my end semester final project, Professor asked our team to develop a voice classification neural network. I don’t have any idea where should I start from. Classification types are laughing , stammering, crying , angry voice , gender of the speaker, age group, etc.
please give me some advices.
Where should I start from ?
Any existing projects or services ? (So far I haven’t found any).
Any similar projects to refer ?
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In April, we introduced several updates to Timelapse, including two additional years of imagery to the time-series visualization, which now spans from 1984 to 2018, with visual upgrades that make exploring more accessible and intuitive. We are especially excited that this update includes support for mobile and tablet devices, which are quickly overtaking desktop computers as the dominant source of app traffic.
Building the Global Visualization
Making a planetary-sized time-lapse video required a significant amount of pixel crunching in Earth Engine, Google’s cloud platform for petabyte-scale geospatial analysis. The new release followed a process similar to what we did in 2013, but at a significantly greater scale—turning 15 million satellite images acquired over the last three and a half decades from the USGS/NASA Landsat and European Sentinel programs into 35 cloud-free 4-terapixel images of the planet—one for each year from 1984 to 2018.
At its native resolution, the Timelapse visualization is a 4 terapixel video (that’s four trillion pixels), which would take about 12 days to download on a 95 Mb/s internet connection. Most computers would have difficulty playing a video of this size, let alone with an interactive, zoomable interface. The problem is even more severe for a mobile device.
A solution was pioneered by Google Maps in 2004 with the map pyramiding technique. Before that time, navigating a map required the use of directional arrows to pan and zoom, with each step requiring the page to reload. The map pyramiding technique assembles the full map image displayed on-screen from tens of small 256×256 pixel non-overlapping image tiles in an array, with new tiles fetched as needed at an appropriate resolution as the user pans and zooms across the map.
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| A traditional Mercator map pyramid contains non-overlapping image tiles. |
This works very well for maps made of static images, but less so for pyramids of video tiles, such as those used by Timelapse, since it requires a web browser to keep up to 16 videos in sync while interacting with the visualization. The solution is embodied in CREATE Lab’s open source Time Machine software: create much larger video tiles that can cover the entire screen and only show one whole-screen tile at a time. The tiles create a pyramid, where sibling tiles overlap with their neighbors to provide a seamless transition between tiles while panning and zooming. Though the overlapping tiles require the use of about 16x more videos, this pyramid structure enables the use of Timelapse on mobile devices by minimizing the amount of data required for visualization.
In our newest release, the global video pyramid consists of 83 million videos across 13 zoom levels, which required about 2 million CPU hours distributed across thousands of machines in Google Cloud to generate.
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| Earth Timelapse uses a pyramid of overlapping video tiles. |
Time Travel, Wherever You Are
Prior to April’s update, ~30% of visitors to the Timelapse visualization were on mobile devices and didn’t actually experience the visualization; instead they saw a YouTube playlist of locations in Timelapse. Until recently, the hardware and CPUs for phones and tablets could not decode videos fast enough without significant delays when someone attempted to zoom in or pan across a video, making mobile exploration unpleasant, if not impossible. In addition, in order for the visualization to be smooth as you pan and zoom, each video that is loaded must sync to the previously playing video and begin playing automatically. But, until only recently, mobile browser vendors had disabled video autoplay at the browser level for bandwidth reasons.
Now that mobile browser vendors have re-enabled video autoplay, we are able to take advantage of current mobile hardware and CPU capabilities, while leveraging the pyramid mapping technique’s efficient use of data, to enable Timelapse on mobile.
Redesigning Timelapse for Exploration Across Devices
Timelapse is a tool for exploration, so we designed for immersiveness, devoting as much real estate as possible to the map. On the other hand, it’s not just a map, but a map of videos. So we kept controls visible, like pausing and restarting the timeline or choosing highlights, by leveraging Material Design with simple, clean lines and clear focal areas.
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| Navigate with Google Maps using the new “Maps Mode” toggle. |
To explore, you need to know where you are or where somewhere else is, so the new interface includes a new “Maps Mode” toggle that lets the user navigate with Google Maps. We also built in scalability to the timeline element of the UI, so that new features added in the future, such as lengthening the time-lapse or adding options for different time increments, won’t break the design. The timeline also allows the user to go backwards in time—an interesting way to compare the present with the past.
For desktop browsers supporting WebGL, we also added a new WebGL viewer to the open source project, which loads and synchronizes multiple videos to fill the screen at optimal resolution. The aesthetic improvement of this is nontrivial, with >4x better resolution.
What’s next
We’re excited about the abundance of freely available, openly licensed satellite imagery and remote sensing data available, enabling new visualizations across time, space, and the visual and non-visual spectrum. We’ve found it’s often the data combined with supplemental layers, such as the World Database on Protected Areas (WDPA) boundaries, that can spark new insights. For example, seeing the visual connection between declining home ownership and shifts in the city of Pittsburgh’s racial makeup tells a story about inequality that numbers on a page simply cannot. Visual evidence can transcend language and cultural barriers and, we hope, generate productive conversations about our global challenges.
Acknowledgements
Randy Sargent, Senior Systems Scientist, Carnegie Mellon University CREATE Lab and the Google Earth Engine team
Hi,
I have a noisy text corpus from twitter. I want to tokenize it efficiently to train language models ( e.g. GPT ).
Most of the sentences have :
1) Spelling errors
2) Emojis
3) Slang spellings ( eg great -> gr8 )
4) All sorts of weird stuff
Any script/tool which takes care of all kinds of cases and works for all kinds of English text.
Thank you
submitted by /u/svufzafa
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JS – A tool to automatically generate all the training data that we need…the problem is, however, development of this tool will likely need training data as well.
FT – Agree with all of the above. But in particular for Conversational AI (where I spend most of my time with Watson Assistant) – any automation tool that could take a client’s data and automatically build out intent/entity recognition AND the dialog.
RP – Once you are in the trenches, you realize, it all starts from data. I wish we had a tool that takes noisy data and makes it clear for AI – all automatically. Enterprises soon realize, they spend most of the time in getting data ready for AI, from different formats, in different places with different permission, with tons of noise. An automation tool to make that “look ma – no hands” will be great!
I am thinking, what sort of model would you use for this? What training data would you feed it? How would you generate that data?
submitted by /u/BatmantoshReturns
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I improve https://arxiv.org/abs/1705.03122 architecture to use dilated convolution based and change the attention mechanism. In small dataset, I beat that model in term of word position accuracy, never tested on bigger dataset (right now tested on 500 English-Vietnam Language Translation dataset), I would like to if I can get some guide to publish the results on research paper.
Accuracy after 20 epoch on convolution seq2seq, epoch: 20, avg loss: 5.096660, avg accuracy: 0.177135
Accuracy after 20 epoch on dilated convolution seq2seq, epoch: 20, avg loss: 0.009440, avg accuracy: 1.023615
Pardon about the accuracy more than 1.0, that one just a bug from my code, can fix it less than a second.
Again, I never tested on bigger dataset.
My question is, I am totally have no knowledge about publishing, googling seems no exact step-by-step, so can I get any links / tips / guide, thanks!
submitted by /u/huseinzol05
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https://link.springer.com/article/10.1007%2Fs00034-002-2007-7
It seems a faster way of learning or training models.
This can be used for IoT applications where online learning can be challenging in terms of power and speed (performance).
I understand that this has limitations when scaled up. But can anyone tell me more about demerits of this classification algorithm.
submitted by /u/Abhishek_nair_1303
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