Discriminative Active Learning
By Daniel Gissin, Shai Shalev-Shwartz. 2019
193 citations recorded by the source corpus.
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 when the batch size is relatively large, negating some of the reported results in the recent literature.
Subfields: Artificial Intelligence, Representation & Self-Supervised Learning
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Geoffrey Irving
- Jeff Wu
- Sergey Levine
- Luke S. Zettlemoyer
- Yejin Choi
- Quoc V. Le
- Paul Christiano
- Daniel M. Ziegler
In Representation & Self-Supervised Learning
- Nisan Stiennon
- Xiaojun Chang
- Paul Christiano
- Daniel M. Ziegler
- Xin Wang
- Po-Yao (Bernie) Huang
- Xiaojiang Chen
- Dario Amodei
Citations in the corpus
- Fine-Tuning Language Models from Human Preferences (2019, background): “Examples of such techniques include selecting batches based on entropy considerations (Guo and Schuurmans, 2008), gradient-based metrics (Huang et al., 2016; Ash et al., 2019), or by attempting to distinguish labeled and unlabeled examples (Gissin and Shalev-Shwartz, 2019).”
- Uncertainty Estimation for Language Reward Models (2022, background): “While simple, this has been found to be a strong baseline [38, 49, 14].”
- A Survey of Deep Active Learning (2020, methodology): “It should be noted here that while DBAL [72] allows the use of dropout in testing for better confidence estimation, the analysis presented in [79] contends that the performance of this method is similar to the performance of using neural network SR [229] as uncertainty sampling, which requires vigil”