# Deep Active Learning for Computer Vision Tasks: Methodologies, Applications, and Challenges
## 4 Deep Active Learning Methods
### Semi-Supervised Active Learning  
Vision tasks based on supervised learning require a large amount of labeled data for model training. These labeled data not only provide direct supervision signals but also limit the generalization ability of the model [69]. At the same time, the acquisition of these \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Methods** & **Innovation** & **Architecture** & **Comments** \\ \hline \multirow{8}{*}{GAAL} & \multirow{8}{*}{\(\rhd\) The first novel query synthesis-based active learning method GAAL fused with GAN.} & \multirow{8}{*}{\(\rhd\) Generator: CNN} & \multirow{8}{*}{\(\rhd\) GAAL provided rich representation training samples for active learning via GAN.} \\  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \hline \multirow{8}{*}{BGAL} & \multirow{8}{*}{\(\rhd\) BGAL integrated deep active learning and data augmentation methods to generate informative samples and expand the labeled data set to improve the accuracy of model classification.} & \multirow{8}{*}{\(\rhd\) BGAL also integrated ACGAN [67] and VAE-GAN [68] into a novel generative model named VAE-ACGAN, where the VAE decoder was the generator of the GAN.} & \multirow{8}{*}{\(\rhd\) BGAL extended the GAAL by \\  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \hline \multirow{8}{*}{ASAAL} & \multirow{8}{*}{\(\rhd\) ASAAL consists of uncertainty sampling, adversarial sample generation, and sample matching.} & \multirow{8}{*}{\(\rhd\) In order to approximate the underlying data distribution from the unlabeled data pool, ASAAL utilized a GAN to generate adversarial samples.} & \multirow{8}{*}{\(\rhd\) ASAAL designed an efficient matching algorithm, where an uncertainty score was calculated to measure the similarity between the unlabeled samples and the generated samples.} & \multirow{8}{*}{\(\rhd\) ASAAL selected the most similar samples from the pool and performs annotation.} & \multirow{8}{*}{\(\rhd\) ASAAL was the first pool-based generative active learning method.} & \multirow{8}{*}{\(\rhd\) Generator: CNN with matching method.} & \multirow{8}{*}{\(\rhd\) The main contribution of ASAAL was to select the most similar sample from pool instead of directly annotating it via a matching algorithm.} & \multirow{8}{*}{\(\rhd\) ASAAL utilized the entropy for uncertainty estimation and was applied in the multi-label classification.} \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \hline \multirow{8}{*}{VAAL} & \multirow{8}{*}{\(\rhd\) VAAL utilized adversarial learning to promote active learning.} & \multirow{8}{*}{\(\rhd\) A variational autoencoder (VAE) was used to extract image features, and then a discriminator decided whether the image was labeled or unlabeled.} & \multirow{8}{*}{\(\rhd\) Generator: VAE} \\  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of generative methods in deep active learning. data is challenging due to the cost and time of annotation [26]. To alleviate this limitation, methods based on semi-supervised learning have become another mainstream. It studies model training under the premise of limited labeled data. It expects higher performance to balance the dilemma between performance and cost, which is a perfect fit for active learning, thus bringing a lot of research and practical value. Semi-supervised learning (SSL) can train the model with a small amount of labeling cost. Unlike active learning, SSL methods usually select samples with confident prediction results instead of uncertain samples and then label them directly by the model instead of annotators.