# Deep Active Learning for Computer Vision Tasks: Methodologies, Applications, and Challenges
## 2 Candidate Selection Strategies in Active Learning
### Committee-Based Selection Strategy  
Committee-based selection strategy [23] is based on version space reduction, and its core idea is to preferentially select unlabeled samples that can reduce the version space to  
Figure 1: Illustrations of different candidate selection strategies in active learning. The dashed line represents the classification hyper-plane of the existing model. The hollow circles represent unlabeled data, the colored circles represent labeled data, and the gray circles represent selected candidates. The gray circle in subfigure (**a**) represents the least confident sample selected by the uncertainty-based strategy. In subfigure (**b**), the three gray circles represent the most representative samples selected by the diversity-based strategy. the greatest extent. The committee-based selection strategy's motivation is that the most informative selections are the samples where the committee predicts the most inconsistent. Typical committee-based selection strategies include vote entropy and average KL divergence, which are listed in Table 2. There are four basic steps in the committee-based selection strategy:  
1. Multiple models \(\{\mathcal{M}_{1},\ldots,\mathcal{M}_{N}\}\) are used to construct a committee \(\mathcal{C}\) for voting, i.e., \(\mathcal{C}=\{\mathcal{M}_{1},\ldots,\mathcal{M}_{N}\}\). 2. The models in the committee \(\mathcal{C}\) are then trained on the labeled dataset \(L\) and get different parameters. 3. All models in the committee make predictions separately on unlabeled samples from \(\mathcal{U}\). The samples with the richest information are voted. 4. The samples which obtain the most disagreements are selected as candidates for labeling. **3. Common Querying Scenarios in Active Learning**  
According to the application scenarios, active learning methods can be divided into three types: query synthesis scenario, stream-based scenario, and pool-based scenario. We briefly summarize the above querying scenarios in Table 3 and introduce the pool-based scenario in detail. Among the above mainstreams, pool-based active learning is more compatible with batch-based modern deep learning training mechanisms. Compared with the stream-based selective sampling, the pool-based scenario is able to consider every sample based on \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Scenarios** & **Concepts** & **Limitations** & **Publications** \\ \hline Membership query synthesis & \(\rhd\) The membership query synthesis is to generate new unlabeled instances for querying by itself instead of selecting samples from the real-world distribution [27]. & \(\rhd\) It may encounter troubles when the generated data is too arbitrary for the annotator to recognize or does not contains any semantic information. & [28; 29] \\ \hline Stream-based sampling & \(\rhd\) The stream-based scenario [30] is to sample from the natural distribution instead of the synthesized one. & \(\rhd\) It is necessary for the model to immediately decide based on a single input rather than the comprehensive consideration of this batch. & [31; 32; 33] \\ \hline Pool-based sampling & \(\rhd\) The pool-based sampling scenario is to selects the most valuable samples from an unlabeled data pool for labeling according to the informativeness [34]. & \(\rhd\) It is computationally expensive because every iteration requires the informativeness [35; 36; 37] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of common querying scenarios in active learning. this batch comprehensively. Consequently, it has become the most common scenario in computer vision tasks. Moreover, the most related works introduced in this review also belong to the pool-based active learning scenario. Figure 2 is a classic pool-based active learning framework. A single batch of unlabeled samples is input to the model from the unlabeled data pool during the training process. Then, the query strategy selects the most valuable samples for labeling according to the informativeness.