Variational Adversarial Active Learning

Our model learns the distribution of labeled data in a latent space using a VAE optimized using both reconstruction and adversarial losses. A binary classifier predicts unlabeled examples and sends them to an oracle for annotations. The VAE is trained to fool the adversarial network to believe that all the examples are from the labeled data while the adversarial classifier is trained to differentiate labeled from unlabeled samples.


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. Our method learns a latent space using a variational autoen- coder (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 la- beled pool, the adversarial network learns how to discrim- inate between dissimilarities in the latent space. We exten- sively evaluate our method on various image classification and semantic segmentation benchmark datasets and estab- lish a new state of the art on CIFAR10/100, Caltech-256, ImageNet, Cityscapes, and BDD100K. Our results demon- strate that our adversarial approach learns an effective low dimensional latent space in large-scale settings and pro- vides for a computationally efficient sampling method.



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