Diverse mini-batch Active Learning

Abstract

We study the problem of reducing the amount of labeled training data required to train supervised classification models. We approach it by leveraging Active Learning, through sequential selection of examples which benefit the model most. Selecting examples one by one is not practical for the amount of training examples required by the modern Deep Learning models. We consider the mini-batch Active Learning setting, where several examples are selected at once. We present an approach which takes into account both informativeness of the examples for the model, as well as the diversity of the examples in a mini-batch. By using the well studied K-means clustering algorithm, this approach scales better than the previously proposed approaches, and achieves comparable or better performance.

Cited by in corpus (13)

• Batch Active Learning at Scale
• Deep Indexed Active Learning for Matching Heterogeneous Entity Representations
• Deep Active Learning for Computer Vision: Past and Future
• Bayesian Estimate of Mean Proper Scores for Diversity-Enhanced Active Learning
• Novel Batch Active Learning Approach and Its Application to Synthetic Aperture Radar Datasets
• Deep Active Learning by Leveraging Training Dynamics
• Multi-class Text Classification using BERT-based Active Learning
• Deep Active Learning for Sequence Labeling Based on Diversity and Uncertainty in Gradient
• Less is more: Selecting informative and diverse subsets with balancing constraints
• Sample Noise Impact on Active Learning
• Few-Shot Image-to-Semantics Translation for Policy Transfer in Reinforcement Learning
• Active Learning for Argument Mining: A Practical Approach
• On Data Efficiency of Meta-learning