Semi-Supervised Variational Adversarial Active Learning via Learning to Rank and Agreement-Based Pseudo Labeling

TL;DR

This paper introduces a semi-supervised active learning method that enhances unlabeled data utilization and incorporates task information through a novel pseudo-labeling and ranking-based loss prediction, improving performance on image tasks.

Contribution

It proposes a new semi-supervised variational adversarial active learning framework with a ranking-based loss and agreement-based pseudo-labeling, addressing VAAL's limitations.

Findings

Outperforms state-of-the-art on image classification benchmarks

Effectively leverages unlabeled data during training

Improves sample selection with ranking-based loss

Abstract

Active learning aims to alleviate the amount of labor involved in data labeling by automating the selection of unlabeled samples via an acquisition function. For example, variational adversarial active learning (VAAL) leverages an adversarial network to discriminate unlabeled samples from labeled ones using latent space information. However, VAAL has the following shortcomings: (i) it does not exploit target task information, and (ii) unlabeled data is only used for sample selection rather than model training. To address these limitations, we introduce novel techniques that significantly improve the use of abundant unlabeled data during training and take into account the task information. Concretely, we propose an improved pseudo-labeling algorithm that leverages information from all unlabeled data in a semi-supervised manner, thus allowing a model to explore a richer data space. In addition, we develop a ranking-based loss prediction module that converts predicted relative ranking information into a differentiable ranking loss. This loss can be embedded as a rank variable into the latent space of a variational autoencoder and then trained with a discriminator in an adversarial fashion for sample selection. We demonstrate the superior performance of our approach over the state of the art on various image classification and segmentation benchmark datasets.