Discriminative Active Learning

Daniel Gissin, Shai Shalev-Shwartz

Abstract: We propose a new batch mode active learning algorithm designed for neural networks and large query batch sizes. The method, Discriminative Active Learning (DAL), poses active learning as a binary classification task, attempting to choose examples to label in such a way as to make the labeled set and the unlabeled pool indistinguishable. Experimenting on image classification tasks, we empirically show our method to be on par with state of the art methods in medium and large query batch sizes, while being simple to implement and also extend to other domains besides classification tasks. Our experiments also show that none of the state of the art methods of today are clearly better than uncertainty sampling, negating some of the reported results in the recent literature.

TL;DR: A new active learning algorithm for the batch mode setting using neural networks

Keywords: Active Learning, Neural Networks

Code: [ dsgissin/DiscriminativeActiveLearning](https://github.com/dsgissin/DiscriminativeActiveLearning) + [ 1 community implementation](https://paperswithcode.com/paper/?openreview=rJl-HsR9KX)

Data: [CIFAR-10](https://paperswithcode.com/dataset/cifar-10), [MNIST](https://paperswithcode.com/dataset/mnist)

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/discriminative-active-learning/code)