Variational Adversarial Active Learning (1904.00370v3)

Published 31 Mar 2019 in cs.LG, cs.CV, and stat.ML

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.

Authors (3)

Samarth Sinha (22 papers)
Sayna Ebrahimi (27 papers)
Trevor Darrell (324 papers)

Citations (536)

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Summary

Variational Adversarial Active Learning: A Summary

The paper, Variational Adversarial Active Learning (VAAL), introduces a novel approach to active learning—a key area in machine learning focused on optimizing the efficiency of data labeling processes. Active learning traditionally aims to reduce the labeling requirements by selectively choosing the most informative data points for annotation. This paper describes a task-agnostic, semi-supervised algorithm that utilizes variational autoencoders (VAEs) and adversarial networks to create an efficient sampling mechanism.

Methodology

VAAL employs a pool-based semi-supervised strategy. The core idea is to utilize a VAE to learn a latent space representation of both labeled and unlabeled data. Meanwhile, an adversarial network is trained to distinguish between these two types of data within the latent space. The interaction between the VAE and the adversarial network is akin to the mini-max game found in GANs. The VAE attempts to 'fool' the adversarial network into classifying all data points as labeled, while the adversarial network learns to accurately distinguish them.

The key innovation here is that the sample selection process is decoupled from the main training task. This task independence allows for a more flexible and theoretically robust approach since the sampling method does not directly rely on task-specific performance metrics.

Results

The paper presents strong empirical results across multiple datasets, establishing new benchmarks. It demonstrates superior performance on image classification tasks using datasets such as CIFAR10, CIFAR100, Caltech-256, and ImageNet, as well as on semantic segmentation tasks using Cityscapes and BDD100K. The approach offers a significant improvement over existing methods, not only in terms of classification accuracy but also in computational efficiency.

For example, on ImageNet—known for its complexity due to the large number of classes—the gap between previous state-of-the-art methods and VAAL's performance is notable. In terms of computational time for sampling, VAAL outperforms many other active learning strategies due to its efficient adversarial setup.

Implications

From a theoretical standpoint, VAAL contributes to enhancing the robustness of active learning methodologies by effectively utilizing VAEs to capture latent representations in a task-agnostic manner. Practically, the reduction in required labeled data without compromising accuracy can greatly benefit fields where data annotation is costly or cumbersome, such as medical imaging or autonomous driving scenarios.

Future Directions

The potential for VAAL extends to its application in various domains requiring efficient data labeling. Further research may explore the adaptability of this framework to different types of data and tasks, as well as the integration of VAAL with other advanced generative models. Additionally, enhancing the robustness of the approach in environments with high noise or highly imbalanced class distributions could be lucrative areas for future exploration.

Overall, VAAL represents a significant step forward in the evolution of active learning, offering a compelling blend of theoretical sophistication and practical utility.