Active Learning for Multi-class Image Classification
This work addresses the data efficiency problem for image classification practitioners, but it is incremental as it applies existing active learning methods to standard datasets.
The paper tackled the problem of reducing the number of training examples needed for image classification by using active learning with uncertainty metrics, demonstrating results on MNIST and Fruits360 datasets and showing that improvement over random sampling is more evident on difficult tasks.
A principle bottleneck in image classification is the large number of training examples needed to train a classifier. Using active learning, we can reduce the number of training examples to teach a CNN classifier by strategically selecting examples. Assigning values to image examples using different uncertainty metrics allows the model to identify and select high-value examples in a smaller training set size. We demonstrate results for digit recognition and fruit classification on the MNIST and Fruits360 data sets. We formally compare results for four different uncertainty metrics. Finally, we observe active learning is also effective on simpler (binary) classification tasks, but marked improvement from random sampling is more evident on more difficult tasks. We show active learning is a viable algorithm for image classification problems.