Timezone: »

 
Poster
SIMILAR: Submodular Information Measures Based Active Learning In Realistic Scenarios
Suraj Kothawade · Nathan Beck · Krishnateja Killamsetty · Rishabh Iyer

Fri Dec 10 08:30 AM -- 10:00 AM (PST) @ Virtual
in Poster Session 8 »

Active learning has proven to be useful for minimizing labeling costs by selecting the most informative samples. However, existing active learning methods do not work well in realistic scenarios such as imbalance or rare classes,out-of-distribution data in the unlabeled set, and redundancy. In this work, we propose SIMILAR (Submodular Information Measures based actIve LeARning), a unified active learning framework using recently proposed submodular information measures (SIM) as acquisition functions. We argue that SIMILAR not only works in standard active learning but also easily extends to the realistic settings considered above and acts as a one-stop solution for active learning that is scalable to large real-world datasets. Empirically, we show that SIMILAR significantly outperforms existing active learning algorithms by as much as ~5%−18%in the case of rare classes and ~5%−10%in the case of out-of-distribution data on several image classification tasks like CIFAR-10, MNIST, and ImageNet.

Keywords: Machine Learning · Vision · Active Learning

Author Information

Suraj Kothawade (University of Texas at Dallas)
Nathan Beck (University of Texas, Dallas)
Krishnateja Killamsetty (University of Texas, Dallas)
Rishabh Iyer (University of Texas, Dallas)

Bio: Prof. Rishabh Iyer is currently an Assistant Professor at the University of Texas, Dallas, where he leads the CARAML Lab. He is also a Visiting Assistant Professor at the Indian Institute of Technology, Bombay. He completed his Ph.D. in 2015 from the University of Washington, Seattle. He is excited in making ML more efficient (both computational and labeling efficiency), robust, and fair. He has received the best paper award at Neural Information Processing Systems (NeurIPS/NIPS) in 2013, the International Conference of Machine Learning (ICML) in 2013, and an Honorable Mention at CODS-COMAD in 2021. He has also won a Microsoft Research Ph.D. Fellowship, a Facebook Ph.D. Fellowship, and the Yang Award for Outstanding Graduate Student from the University of Washington.

More from the Same Authors