# Deep Active Learning for Computer Vision Tasks: Methodologies, Applications, and Challenges
## 4 Deep Active Learning Methods
### Generative Adversarial Active Learning
1\(\mathcal{M}\) initialization; repeat
2 Train the model \(\mathcal{M}\) with the labeled dataset \(L\) is fed into the for supervised training;
3 Train the generator \(\mathbb{G}\) and discriminator \(\mathbb{D}\) with all unlabeled dataset \(\mathcal{U}\) via Equation (6);
4 Synthesize instances with the generator \(\mathbb{G}\) for querying;
5 Obtain the representation \(\mathcal{R}\) of all synthesized instances;
6 Query the top-K synthesized instances \(\mathcal{K}\) via uncertain strategies, according to the representation \(\mathcal{R}\);
7 Label \(\mathcal{K}\) from the annotators as the ground truth \(Y^{K}\);
8 Update \(L=L\cup\{\mathcal{K},Y^{K}\}\), update \(\mathcal{U}=\mathcal{U}\cup\mathcal{K}\). 9 untilEnd conditions;
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**Algorithm 2**The synthesis-based active learning method workflow  
GAAL inspired GAN-based generative adversarial methods in active learning. Consequently, the latter works were devoted to studying pool-based Generative Adversarial Active Learning. [PERSON] et al. (2019) proposed a framework of Bayesian Generative Active Learning (BGAL) to solve multi-classification tasks when the amount of labeled data is small. The proposed BGAL was validated on MNIST ([PERSON] et al., 2016) (achieving accuracy 99.68%), CIFAR-10/100 ([PERSON] et al., 2016) (achieving accuracy 91.13%), and SVHN ([PERSON] et al., 2017) (achieving accuracy 69.69%). [PERSON] et al. (2017) proposed a pool-based active learning strategy(ASAL). Compared to traditional pool-based strategies for exhaustive uncertainty search from unlabeled pools, ASAL utilized GAN to generate the most representative samples from unlabeled pools, resulting in more efficient active learning techniques. ASAL was validated on the datasets MNIST ([PERSON] et al., 2016) (reducing 300 labeled samples), CIFAR-10 ([PERSON] et al., 2016) (reducing 500 labeled samples), CelebA ([PERSON] et al., 2016) (reducing 750 labeled samples), SVHN ([PERSON] et al., 2017), and LSUN Scenes ([PERSON] et al., 2017). [PERSON] et al. (2017) proposed a pool-based semi-supervised active learning algorithm VAAL. VAAL obtained great improvement in experimental results on classification and segmentation. VAAL achieved great improvement in experimental results, including CIFAR10/100 ([PERSON] et al., 2016) (achieving accuracy of 90.16%/63.14%), Caltech-256 ([PERSON] et al., 2016) (achieving 1.01% improvement on Coreset), ImageNet ([PERSON] et al., 2017), Cityscapes ([PERSON] et al., 2017) (achieving mIoU 62.95), and BDD100K ([PERSON] et al., 2016) (achieving mIoU 44.95). The above methods were devoted to directly generating the representative samples by solving some optimization problems, and then improving the efficiency of screening samples for active learning. More details are summarized in Table 5. Apart from that, [PERSON] et al. (2017) firstly proposed to use a GAN to generate a batch of samples along the decision boundary of the current classifier. Next, they determined the location where the category changed from the generated samples through visualization and added it to the set of samples to be labeled. Finally, the method's effectiveness was verified by a large number of image classification experiments. The method can reduce the burden of data annotation by requiring the annotator to label decision boundaries instead of samples.