Submodular Mutual Information for Targeted Data Subset Selection
This addresses the challenge of efficiently training models with large datasets for researchers and practitioners in machine learning, though it is incremental as it builds on existing subset selection and active learning methods.
The paper tackles the problem of selecting targeted subsets of unlabeled data to improve deep learning models by using Submodular Mutual Information functions, achieving a 20-30% performance gain over baseline models and outperforming other methods by about 12% on CIFAR-10 and MNIST datasets.
With the rapid growth of data, it is becoming increasingly difficult to train or improve deep learning models with the right subset of data. We show that this problem can be effectively solved at an additional labeling cost by targeted data subset selection(TSS) where a subset of unlabeled data points similar to an auxiliary set are added to the training data. We do so by using a rich class of Submodular Mutual Information (SMI) functions and demonstrate its effectiveness for image classification on CIFAR-10 and MNIST datasets. Lastly, we compare the performance of SMI functions for TSS with other state-of-the-art methods for closely related problems like active learning. Using SMI functions, we observe ~20-30% gain over the model's performance before re-training with added targeted subset; ~12% more than other methods.