OFAL: An Oracle-Free Active Learning Framework
This work addresses the challenge of reducing labeling costs in active learning for machine learning practitioners, though it appears incremental as it builds on existing uncertainty-based methods.
The paper tackles the problem of expensive oracle labeling in active learning by introducing OFAL, an oracle-free framework that uses neural network uncertainty and a variational autoencoder to generate informative uncertain samples, achieving improved model accuracy without relying on an oracle.
In the active learning paradigm, using an oracle to label data has always been a complex and expensive task, and with the emersion of large unlabeled data pools, it would be highly beneficial If we could achieve better results without relying on an oracle. This research introduces OFAL, an oracle-free active learning scheme that utilizes neural network uncertainty. OFAL uses the model's own uncertainty to transform highly confident unlabeled samples into informative uncertain samples. First, we start with separating and quantifying different parts of uncertainty and introduce Monte Carlo Dropouts as an approximation of the Bayesian Neural Network model. Secondly, by adding a variational autoencoder, we go on to generate new uncertain samples by stepping toward the uncertain part of latent space starting from a confidence seed sample. By generating these new informative samples, we can perform active learning and enhance the model's accuracy. Lastly, we try to compare and integrate our method with other widely used active learning sampling methods.