/* ---- Google Analytics Code Below */

Wednesday, October 03, 2018

Deep Learning without Labels for Apache Spark

With a quite instructive visual example with the Snow Leopard images.

Machine Learning Blog

Deep Learning Without Labels    by ML Blog Team 

Announcing new open source contributions to the Apache Spark community for creating deep, distributed, object detectors – without a single human-generated label

This post is authored by members of the Microsoft ML for Apache Spark Team – Mark Hamilton, Minsoo Thigpen, Abhiram Eswaran, Ari Green, Courtney Cochrane, Janhavi Suresh Mahajan, Karthik Rajendran, Sudarshan Raghunathan, and Anand Raman. 

In today’s day and age, if data is the new oil, labelled data is the new gold.

Here at Microsoft, we often spend a lot of our time thinking about “Big Data” issues, because these are the easiest to solve with deep learning. However, we often overlook the much more ubiquitous and difficult problems that have little to no data to train with. In this work we will show how, even without any data, one can create an object detector for almost anything found on the web. This effectively bypasses the costly and resource intensive processes of curating datasets and hiring human labelers, allowing you to jump directly to intelligent models for classification and object detection completely in sillico.

We apply this technique to help monitor and protect the endangered population of snow leopards.

This week at the Spark + AI Summit in Europe, we are excited to share with the community, the following exciting additions to the Microsoft ML for Apache Spark Library that make this workflow easy to replicate at massive scale using Apache Spark and Azure Databricks:

Bing on Spark: Makes it easier to build applications on Spark using Bing search.
LIME on Spark: Makes it easier to deeply understand the output of Convolutional Neural Networks (CNN) models trained using SparkML.

High-performance Spark Serving: Innovations that enable ultra-fast, low latency serving using Spark.

We illustrate how to use these capabilities using the Snow Leopard Conservation use case, where machine learning is a key ingredient towards building powerful image classification models for identifying snow leopards from images.  ... "

No comments: