# Variational Adversarial Active Learning

## TL;DR

This paper introduces a task-agnostic active learning method using a variational autoencoder and adversarial training to efficiently select informative samples, achieving state-of-the-art results on multiple image datasets.

## Contribution

It presents a novel adversarial active learning approach that learns a latent space for sample selection without relying on task-specific performance metrics.

## Key findings

- Achieves state-of-the-art results on CIFAR10/100, Caltech-256, ImageNet, Cityscapes, and BDD100K.
- Learns an effective low-dimensional latent space in large-scale settings.
- Provides a computationally efficient sampling method.

## Abstract

Active learning aims to develop label-efficient algorithms by sampling the most representative queries to be labeled by an oracle. We describe a pool-based semi-supervised active learning algorithm that implicitly learns this sampling mechanism in an adversarial manner. Unlike conventional active learning algorithms, our approach is task agnostic, i.e., it does not depend on the performance of the task for which we are trying to acquire labeled data. Our method learns a latent space using a variational autoencoder (VAE) and an adversarial network trained to discriminate between unlabeled and labeled data. The mini-max game between the VAE and the adversarial network is played such that while the VAE tries to trick the adversarial network into predicting that all data points are from the labeled pool, the adversarial network learns how to discriminate between dissimilarities in the latent space. We extensively evaluate our method on various image classification and semantic segmentation benchmark datasets and establish a new state of the art on $\text{CIFAR10/100}$, $\text{Caltech-256}$, $\text{ImageNet}$, $\text{Cityscapes}$, and $\text{BDD100K}$. Our results demonstrate that our adversarial approach learns an effective low dimensional latent space in large-scale settings and provides for a computationally efficient sampling method. Our code is available at https://github.com/sinhasam/vaal.