Reinforcement Learning Approach to Active Learning for Image Classification

TL;DR

This paper explores framing active learning for image classification as a reinforcement learning problem, proposing a new framework and conducting experiments, but ultimately finds the approach ineffective in its current form.

Contribution

It introduces a novel reinforcement learning framework for active learning in image classification and evaluates its performance through multiple experiments.

Findings

The proposed framework did not improve active learning performance.

Experiments identified issues with the reinforcement learning approach.

Proposed improvements showed limited impact on results.

Abstract

Machine Learning requires large amounts of labeled data to fit a model. Many datasets are already publicly available, nevertheless forcing application possibilities of machine learning to the domains of those public datasets. The ever-growing penetration of machine learning algorithms in new application areas requires solutions for the need for data in those new domains. This thesis works on active learning as one possible solution to reduce the amount of data that needs to be processed by hand, by processing only those datapoints that specifically benefit the training of a strong model for the task. A newly proposed framework for framing the active learning workflow as a reinforcement learning problem is adapted for image classification and a series of three experiments is conducted. Each experiment is evaluated and potential issues with the approach are outlined. Each following experiment then proposes improvements to the framework and evaluates their impact. After the last experiment, a final conclusion is drawn, unfortunately rejecting this work's hypothesis and outlining that the proposed framework at the moment is not capable of improving active learning for image classification with a trained reinforcement learning agent.