Active Learning for Structured Prediction from Partially Labeled Data

by   Mehran Khodabandeh, et al.

We propose a general purpose active learning algorithm for structured prediction, gathering labeled data for training a model that outputs a set of related labels for an image or video. Active learning starts with a limited initial training set, then iterates querying a user for labels on unlabeled data and retraining the model. We propose a novel algorithm for selecting data for labeling, choosing examples to maximize expected information gain based on belief propagation inference. This is a general purpose method and can be applied to a variety of tasks or models. As a specific example we demonstrate this framework for learning to recognize human actions and group activities in video sequences. Experiments show that our proposed algorithm outperforms previous active learning methods and can achieve accuracy comparable to fully supervised methods while utilizing significantly less labeled data.



There are no comments yet.


page 8


ALiPy: Active Learning in Python

Supervised machine learning methods usually require a large set of label...

Combining MixMatch and Active Learning for Better Accuracy with Fewer Labels

We propose using active learning based techniques to further improve the...

Robust Active Learning for Electrocardiographic Signal Classification

The classification of electrocardiographic (ECG) signals is a challengin...

Experimental Design for Overparameterized Learning with Application to Single Shot Deep Active Learning

The impressive performance exhibited by modern machine learning models h...

Graph-based Reinforcement Learning for Active Learning in Real Time: An Application in Modeling River Networks

Effective training of advanced ML models requires large amounts of label...

Active Learning amidst Logical Knowledge

Structured prediction is ubiquitous in applications of machine learning ...

AstronomicAL: An interactive dashboard for visualisation, integration and classification of data using Active Learning

AstronomicAL is a human-in-the-loop interactive labelling and training d...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.