Apr 3, 2019 · post

An Invitation to Active Learning

Many interesting learning problems exist in places where labeled data is limited. As such, much thought has been spent on how best to learn from limited labeled data. One obvious answer is simply to collect more data. That is valid, but for some applications, data is difficult or expensive to collect. If we will collect more data, we ought at least be smart about the data we collect. This motivates active learning, which provides strategies for learning in this scenario.

The ideal setting for active learning is that in which we have a small amount of labeled data with which to build a model and access to a large pool of unlabeled data. We must also have the means to label some of that data, but it’s OK for the labeling process to be costly (for instance, a human hand-labeling an image). The active learning process forms a loop:

  1. build a model based on the labeled data available
  2. use the model to predict labels for the unlabeled points
  3. use an active learning strategy to decide which point to label next
  4. label that point
  5. GOTO 1.

The active learning loop in action The active learning loop in action - try out the demo!

The essence of active learning is in the strategy we choose in the loop above. Three broad families of strategy are:

  • Random sampling. In the default case, we sample unlabeled data from the pool randomly. This is a passive approach where we don’t use the output of the current model to inform the next data point to be labeled. As such, it isn’t really active learning.

  • Uncertainty sampling. In uncertainty sampling, we choose the data point about which the algorithm is least certain to label next. This could be the point closest to the decision boundary (the least confident prediction), or it could be the point with highest entropy, or other measure of uncertainty. Choosing points as such helps our learning algorithm refine the decision boundary.

  • Density sampling. Uncertainty sampling works much better than random sampling, but by definition it causes the data points we choose to label to cluster around the decision boundary. This data may be very informative, but not necessarily representative. In density sampling, we try to sample from regions where there are many data points. The trade off between informativeness and representativeness is fundamental to active learning, and there are many approaches that address it.

To illustrate the difference between passive and active learning, we created an Observable notebook with a toy problem, which you can explore here. In the notebook, the goal is to find a good separation of red and blue points on a two dimensional chart, and we train a logistic regression model live in the browser to do so. One can see how the decision boundary separating the points evolves as more data is labeled with a random sampling strategy, and also with an uncertainty sampling strategy. In the case of two classes with a linear decision boundary, all the uncertainty sampling strategies (least confidence and highest entropy) give the same result. This is an extremely simplified example, but we think it shows some of the intuition behind active learning.

We explore active learning in much more detail in our report Learning with Limited Labeled Data, and you can get a high level overview in our previous post.

Read more

Apr 29, 2019 · newsletter
Apr 2, 2019 · featured post

Latest posts

Nov 15, 2022 · newsletter

CFFL November Newsletter

November 2022 Perhaps November conjures thoughts of holiday feasts and festivities, but for us, it’s the perfect time to chew the fat about machine learning! Make room on your plate for a peek behind the scenes into our current research on harnessing synthetic image generation to improve classification tasks. And, as usual, we reflect on our favorite reads of the month. New Research! In the first half of this year, we focused on natural language processing with our Text Style Transfer blog series. more
Nov 14, 2022 · post

Implementing CycleGAN

by Michael Gallaspy · Introduction This post documents the first part of a research effort to quantify the impact of synthetic data augmentation in training a deep learning model for detecting manufacturing defects on steel surfaces. We chose to generate synthetic data using CycleGAN,1 an architecture involving several networks that jointly learn a mapping between two image domains from unpaired examples (I’ll elaborate below). Research from recent years has demonstrated improvement on tasks like defect detection2 and image segmentation3 by augmenting real image data sets with synthetic data, since deep learning algorithms require massive amounts of data, and data collection can easily become a bottleneck. more
Oct 20, 2022 · newsletter

CFFL October Newsletter

October 2022 We’ve got another action-packed newsletter for October! Highlights this month include the re-release of a classic CFFL research report, an example-heavy tutorial on Dask for distributed ML, and our picks for the best reads of the month. Open Data Science Conference Cloudera Fast Forward Labs will be at ODSC West near San Fransisco on November 1st-3rd, 2022! If you’ll be in the Bay Area, don’t miss Andrew and Melanie who will be presenting our recent research on Neutralizing Subjectivity Bias with HuggingFace Transformers. more
Sep 21, 2022 · newsletter

CFFL September Newsletter

September 2022 Welcome to the September edition of the Cloudera Fast Forward Labs newsletter. This month we’re talking about ethics and we have all kinds of goodies to share including the final installment of our Text Style Transfer series and a couple of offerings from our newest research engineer. Throw in some choice must-reads and an ASR demo, and you’ve got yourself an action-packed newsletter! New Research! Ethical Considerations When Designing an NLG System In the final post of our blog series on Text Style Transfer, we discuss some ethical considerations when working with natural language generation systems, and describe the design of our prototype application: Exploring Intelligent Writing Assistance. more
Sep 8, 2022 · post

Thought experiment: Human-centric machine learning for comic book creation

by Michael Gallaspy · This post has a companion piece: Ethics Sheet for AI-assisted Comic Book Art Generation I want to make a comic book. Actually, I want to make tools for making comic books. See, the problem is, I can’t draw too good. I mean, I’m working on it. Check out these self portraits drawn 6 months apart: Left: “Sad Face”. February 2022. Right: “Eyyyy”. August 2022. But I have a long way to go until my illustrations would be considered professional quality, notwithstanding the time it would take me to develop the many other skills needed for making comic books. more
Aug 18, 2022 · newsletter

CFFL August Newsletter

August 2022 Welcome to the August edition of the Cloudera Fast Forward Labs newsletter. This month we’re thrilled to introduce a new member of the FFL team, share TWO new applied machine learning prototypes we’ve built, and, as always, offer up some intriguing reads. New Research Engineer! If you’re a regular reader of our newsletter, you likely noticed that we’ve been searching for new research engineers to join the Cloudera Fast Forward Labs team. more

Popular posts

Oct 30, 2019 · newsletter
Exciting Applications of Graph Neural Networks
Nov 14, 2018 · post
Federated learning: distributed machine learning with data locality and privacy
Apr 10, 2018 · post
PyTorch for Recommenders 101
Oct 4, 2017 · post
First Look: Using Three.js for 2D Data Visualization
Aug 22, 2016 · whitepaper
Under the Hood of the Variational Autoencoder (in Prose and Code)
Feb 24, 2016 · post
"Hello world" in Keras (or, Scikit-learn versus Keras)


In-depth guides to specific machine learning capabilities


Machine learning prototypes and interactive notebooks

ASR with Whisper

Explore the capabilities of OpenAI's Whisper for automatic speech recognition by creating your own voice recordings!


A usable library for question answering on large datasets.

Explain BERT for Question Answering Models

Tensorflow 2.0 notebook to explain and visualize a HuggingFace BERT for Question Answering model.

NLP for Question Answering

Ongoing posts and code documenting the process of building a question answering model.

Cloudera Fast Forward Labs

Making the recently possible useful.

Cloudera Fast Forward Labs is an applied machine learning research group. Our mission is to empower enterprise data science practitioners to apply emergent academic research to production machine learning use cases in practical and socially responsible ways, while also driving innovation through the Cloudera ecosystem. Our team brings thoughtful, creative, and diverse perspectives to deeply researched work. In this way, we strive to help organizations make the most of their ML investment as well as educate and inspire the broader machine learning and data science community.

Cloudera   Blog   Twitter

©2022 Cloudera, Inc. All rights reserved.