• Home
  • About Us
  • Contact Us
  • Privacy Policy
  • Special Offers
Business Intelligence Info
  • Business Intelligence
    • BI News and Info
    • Big Data
    • Mobile and Cloud
    • Self-Service BI
  • CRM
    • CRM News and Info
    • InfusionSoft
    • Microsoft Dynamics CRM
    • NetSuite
    • OnContact
    • Salesforce
    • Workbooks
  • Data Mining
    • Pentaho
    • Sisense
    • Tableau
    • TIBCO Spotfire
  • Data Warehousing
    • DWH News and Info
    • IBM DB2
    • Microsoft SQL Server
    • Oracle
    • Teradata
  • Predictive Analytics
    • FICO
    • KNIME
    • Mathematica
    • Matlab
    • Minitab
    • RapidMiner
    • Revolution
    • SAP
    • SAS/SPSS
  • Humor

Researchers are training image-generating AI with fewer labels

March 8, 2019   Big Data

Generative AI models have a propensity for learning complex data distributions, which is why they’re great at producing human-like speech and convincing images of burgers and faces. But training these models requires lots of labeled data, and depending on the task at hand, the necessary corpora are sometimes in short supply.

The solution might lie in an approach proposed by researchers at Google and ETH Zurich. In a paper published on the preprint server Arxiv.org (“High-Fidelity Image Generation With Fewer Labels“), they describe a “semantic extractor” that can pull out features from training data, along with methods of inferring labels for an entire training set from a small subset of labeled images. These self- and semi-supervised techniques together, they say, can outperform state-of-the-art methods on popular benchmarks like ImageNet.

“In a nutshell, instead of providing hand-annotated ground truth labels for real images to the discriminator, we … provide inferred ones,” the paper’s authors explained.

In one of several unsupervised methods the researchers posit, they first extract a feature representation — a set of techniques for automatically discovering the representations needed for raw data classification — on a target training dataset using the aforementioned feature extractor. They then perform cluster analysis — i.e., grouping the representations in such a way that those in the same group share more in common than those in other groups. And lastly, they train a GAN — a two-part neural network consisting of generators that produce samples and discriminators that attempt to distinguish between the generated samples and real-world samples — by inferring labels.

 Researchers are training image generating AI with fewer labels

Above: More sample images generated by the AI systems.

In another pretraining method, dubbed “co-training,” the paper’s authors leverage a combination of unsupervised, semi-supervised, and self-supervised methods to infer label information concurrent with GAN training. During the unsupervised step, they take one of two approaches: completely removing the labels, or assigning random labels to real images. By contrast, in the semi-supervised stage, they train a classifier on the feature representation of the discriminator when labels are available for a subset of the real data, which they use to predict labels for the unlabeled real images.

To test the techniques’ performance, the researchers tapped ImageNet — a database containing over 1.3 million training images and 50,000 test images, each corresponding to one of 1,000 object classes — and obtained partially labeled datasets by randomly selecting a portion of the samples from each image class (i.e., “firetrucks,” “mountains,” etc.).  After training every GAN three times on 1,280 cores of a third-generation Google tensor processing unit (TPU) pod using the unsupervised, pre-trained, and co-training approaches, they compared the quality of the outputs with two scoring metrics: Frechet Inception Distance (FID) and Inception Score (IS).

The unsupervised methods weren’t particularly successful — they achieved a FID and IS of around 25 and 20, respectively, compared with the baseline of 8.4 and 75. Pretraining using self-supervision and clustering reduced FID by 10 percent and increased ID by about 10 percent, and the co-trained method obtained an FID of 13.9 and an IS of 49.2. But by far the most successful was self-supervision: It achieved “state-of-the-art” performance with 20 percent labeled data.

In the future, the researchers hope to investigate how the techniques might be applied to “larger” and “more diverse” datasets. “There are several important directions for future work,” they wrote, “[but] we believe that this is a great first step towards the ultimate goal of few-shot high-fidelity image synthesis.”

Let’s block ads! (Why?)

Big Data – VentureBeat

a Training, Fewer, imagegenerating, labels, researchers
  • Recent Posts

    • NOW, THIS IS WHAT I CALL AVANTE-GARDE!
    • Why the open banking movement is gaining momentum (VB Live)
    • OUR MAGNIFICENT UNIVERSE
    • What to Avoid When Creating an Intranet
    • Is Your Business Ready for the New Generation of Analytics?
  • Categories

  • Archives

    • January 2021
    • December 2020
    • November 2020
    • October 2020
    • September 2020
    • August 2020
    • July 2020
    • June 2020
    • May 2020
    • April 2020
    • March 2020
    • February 2020
    • January 2020
    • December 2019
    • November 2019
    • October 2019
    • September 2019
    • August 2019
    • July 2019
    • June 2019
    • May 2019
    • April 2019
    • March 2019
    • February 2019
    • January 2019
    • December 2018
    • November 2018
    • October 2018
    • September 2018
    • August 2018
    • July 2018
    • June 2018
    • May 2018
    • April 2018
    • March 2018
    • February 2018
    • January 2018
    • December 2017
    • November 2017
    • October 2017
    • September 2017
    • August 2017
    • July 2017
    • June 2017
    • May 2017
    • April 2017
    • March 2017
    • February 2017
    • January 2017
    • December 2016
    • November 2016
    • October 2016
    • September 2016
    • August 2016
    • July 2016
    • June 2016
    • May 2016
    • April 2016
    • March 2016
    • February 2016
    • January 2016
    • December 2015
    • November 2015
    • October 2015
    • September 2015
    • August 2015
    • July 2015
    • June 2015
    • May 2015
    • April 2015
    • March 2015
    • February 2015
    • January 2015
    • December 2014
    • November 2014
© 2021 Business Intelligence Info
Power BI Training | G Com Solutions Limited