Deep Active Learning for Computer Vision Tasks: Methodologies, Applications, and Challenges
Abstract
:1. Introduction
Algorithm 1: The pool-based active learning workflow |
2. Candidate Selection Strategies in Active Learning
2.1. Random Selection Strategies
2.2. Uncertainty-Based Selection Strategies
2.3. Diversity-Based Selection Strategy
2.4. Committee-Based Selection Strategy
- Multiple models are used to construct a committee for voting, i.e., .
- The models in the committee are then trained on the labeled dataset L and get different parameters.
- All models in the committee make predictions separately on unlabeled samples from . The samples with the richest information are voted.
- The samples which obtain the most disagreements are selected as candidates for labeling.
3. Common Querying Scenarios in Active Learning
4. Deep Active Learning Methods
4.1. Deep Active Learning for CNNs
4.2. Generative Adversarial Active Learning
Algorithm 2: The synthesis-based active learning method workflow |
4.3. Semi-Supervised Active Learning
Algorithm 3: The workflow of basic self-training algorithm |
4.4. Active Contrastive Learning
Algorithm 4: The workflow of basic contrastive active learning algorithm |
4.5. Other Deep Active Learning
5. Applications
5.1. Deep Learning-Based Autonomous Driving
5.2. Intelligent Medical Assisted Diagnosis
6. Challenges
6.1. Inefficient Serial Human-in-the-Loop Collaboration
6.2. Dirty Data and Noisy Oracle
6.3. Difficult to Cross-Domain Transfer
6.4. Unstable Performance
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Title | Main Contents | Publication |
---|---|---|
Samuel Budd et al. A survey on active learning and human-in-the-loop deep learning for medical image analysis [6] | ⊳ Investigate the active learning in the medical image analysis. ⊳ Propose the considerations in the deep learning–based active learning, including noisy oracles, weakly supervised learning, multi-task learning, annotation interface, and variable learning costs. ⊳ Discuss the future prospective and unanswered questions in the medical image analysis. | Medical Image Analysis. 2021, 71, 102062 |
Punit Kumar et al. Active Learning Query Strategies for Classification, Regression, and Clustering: A Survey [7] | ⊳ Summarize the active learning query strategies for three tasks, including classification, regression, and clustering. ⊳ Classify the query strategies under classification into: informative-based, representative-based, informative-and-representative-based, and others. ⊳ Summarize the empirical evaluation of active learning query strategies. ⊳ Present the implementation, application, and challenges of the active learning in brief. | Journal of Computer Science and Technology. 2020, 35, 913–945. |
Pengzhen Ren et al. A Survey of Deep Active Learning [8] | ⊳ Classify the existing works in the deep active learning. ⊳ Summarize the deep active learning applications, including vision and NLP. ⊳ Especially, in the visual data processing tasks, it discusses image classification and recognition, object detection and semantic segmentation, and video processing. | ACM Computing Surveys. 54.9 (2021): 1–40. |
Xueying Zhan et al. A Comparative Survey of Deep Active Learning [9] | ⊳ Categorize deep active learning sampling methods and querying strategies. ⊳ Compare deep active learning algorithms across common used datasets. ⊳ Conduct experiments to explore influence factors of deep active learning ⊳ Release a deep active learning toolkit, named DeepAL+. | arXiv:2203.13450, 2022. |
Strategies | Methodologies | Typical Works |
---|---|---|
Random selection | ⊳ Random sampling is to use random numbers to select samples from the unlabeled dataset for labeling. | N.A. |
⊳ | ||
Uncertainty-based selection | ⊳ Least confidence is to select the sample with the smallest probability of the top1 predicted class. In practice, the opposite of the maximum predicted probability is often taken as the uncertainty score of the sample. | Li et al. [10] |
⊳ | Agrawal et al. [11,12] | |
⊳ Margin sampling is to calculate the difference between the probabilities of the top1 and the top2 predicted class.Then the samples with the smallest difference are defined as hard-to-classify samples for labeling. | Ajay J. et al. [13] | |
⊳ | Zhou et al. [14] | |
⊳ Multi-class level uncertainty is to select the two samples that are the farthest from the classification hyperplane of multi-class and take their distance difference as the score. | Gu et al. [15] | |
⊳ | Yang et al. [16] | |
⊳ Maximize entropy is to utilize the methodology that larger entropy denotes higher uncertainty. The sample with the largest entropy is selected as candidate. | Yu et al. [17] | |
⊳ | Ozdemir et al. [18] | |
Diversity-based selection | ⊳ Angle-based measurement is to to measure diversity by calculating the undirected angles between the induced hyper-planes. | Brinker et al. [19] |
⊳ | ||
⊳ Redundancy-based measurement is to measure the diversity as the redundancy between unlabeled points via symmetric KL divergence [20] between the two vectors of probability values. | Shayok et al. [21] | |
⊳ | Zhou et al. [22] | |
Committee-based selection [23] | ⊳ Vote entropy–based measurement is to select the hard sample voted by the Committee. The models in the committee distinguish samples into different classes. The predicted results toward one sample with the largest entropy is classified as hard sample and needs to vote for labeling. | Yan et al. [24] |
⊳ | Dagan et al. [25] | |
⊳ Average KL divergence–based measurement is to measure the deviation of those unlabeled samples via calculating the average KL divergence of the committee . | Dagan et al. [25] | |
Scenarios | Concepts | Limitations | Publications |
---|---|---|---|
Membership query synthesis | ⊳ The membership query synthesis is to generate new unlabeled instances for querying by itself instead of selecting samples from the real-world distribution [27]. | ⊳ 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] |
Stream-based sampling | ⊳ The stream-based scenario [30] is to sample from the natural distribution instead of the synthesized one. ⊳ In this scenario, the selection process is similar to a pipeline. The unlabeled sample is firstly input into the model one by one. ⊳ Then, the active learning strategy needs to decide whether to pass it to the annotator for labeling or reject it directly. | ⊳ It is necessary for the model to immediately decide based on a single input rather than the comprehensive consideration of this batch. ⊳ The active learning system may suffer from the absence of knowledge of unseen areas. | [31,32,33] |
Pool-based sampling | ⊳ The pool-based sampling scenario is to selects the most valuable samples from an unlabeled data pool for labeling according to the informativeness [34]. ⊳ The unlabeled data pool is sampled from the natural distribution instead of synthesized samples. | ⊳ It is computationally expensive because every iteration requires the informativeness evaluation for the whole pool. | [35,36,37] |
Type | Methodology | Equation |
---|---|---|
MC dropout | ⊳ In practice, the MC dropout usually trains the CNN with the labeled data pool L with dropout. ⊳ After training, it generates a new dropout mask for the model parameters and performing T forward inference. ⊳ The output is the average of T results. | |
Deep Ensembles | ⊳ The ensemble-based approaches design N neural networks at first. ⊳ These networks share same architecture but initialized from different weights. ⊳ Then networks are trained with the labeled data pool L. ⊳ The average of the outputs of the N networks is the final output. |
Methods | Innovation | Architecture | Comments |
---|---|---|---|
GAAL [57] | ⊳ The first novel query synthesis-based active learning method GAAL fused with GAN. ⊳ GAAL combined query synthesis with the uncertainty sampling principle and adaptively synthesized training instances for querying to increase learning speed. ⊳ The DCGAN was implemented to replace the unlabeled pool in previous work. | ⊳ Generator: CNN ⊳ Discriminator: CNN ⊳ Predictor: SVM ⊳ Score-function: Uncertainty | ⊳ GAAL was the first work integrated active learning and generative methods. ⊳ GAAL provided rich representation training samples for active learning via GAN. ⊳ GAAL was limited by the generated abnormal instances if the GAN was not optimized correctly. ⊳ GAAL is limited by the binary classification setting. |
BGAL [59] | ⊳ 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. ⊳ 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. ⊳ VAE-ACGAN generated new synthetic instances on the query samples. ⊳ The learner and the VAE-ACGAN were jointly trained in this work. | ⊳ Generator: VAE ⊳ Discriminator: Bayesian CNN ⊳ Predictor: Resnet18 ⊳ Score-function: MC-dropout | ⊳ BGAL extended the GAAL by combined more robust data augmentation techniques. ⊳ The combination of data augmentation and active learning obtained consistent improvement on classification than single methods. ⊳ The computation efficiency need to be improved due to the computational cost is high. |
ASAL [60] | ⊳ ASAL consists of uncertainty sampling, adversarial sample generation, and sample matching. ⊳ In order to approximate the underlying data distribution from the unlabeled data pool, ASAL utilized a GAN to generate adversarial samples. ⊳ ASAL designed an efficient matching algorithm, where an uncertainty score was calculated to measure the similarity between the unlabeled samples and the generated samples. ⊳ ASAL selected the most similar samples from the pool and performs annotation. | ⊳ Generator: CNN with matching ⊳ Discriminator: CNN ⊳ Predictor: CNN ⊳ Score-function: Entropy | ⊳ ASAL was the first pool-based generative active learning method. ⊳ The main contribution of ASAL was to select the most similar sample from pool instead of directly annotating it via a matching algorithm. ⊳ ASAL utilized the entropy for uncertainty estimation and was applied in the multi-label classification. |
VAAL [63] | ⊳ VAAL utilized adversarial learning to promote active learning. ⊳ A variational autoencoder (VAE) was used to extract image features, and then a discriminator decided whether the image was labeled or unlabeled. ⊳ The VAE hoped to trick the discriminator into judging all samples as labeled data, but the discriminator hoped to accurately distinguish unlabeled samples in the data pool. ⊳ The annotator labeled the unlabeled samples selected based on this method. | ⊳ Generator: VAE ⊳ Discriminator: MLP ⊳ Predictor: VGG16 ⊳ Score-function: Confidence | ⊳ VAAL provided a computational efficient sampling method with the best accuracy and time cost. |
Applications | Comments | Implementation | Evaluation |
---|---|---|---|
Autonomous navigation [105] | ⊳ Proposed a framework for learning autonomous policies for navigation tasks from demonstrations. | ⊳ Network: 3 × (Conv + Pool) + FC. ⊳ Score-function Entropy: | ⊳ Reach the flag: error rate = 2.48%. ⊳ Follow the line: error rate = 4.06%. ⊳ Reach the correct object: error rate = 0.86%. ⊳ Eat all disks: error rate = 1.70% |
Weather and light classification [106] | ⊳ Released the first public dataset for weather and light level classification focused on autonomous driving. | ⊳ Target network: Resnet18 ⊳ Loss-prediction module [52]: 4 × (GAP + FC + ReLU) + Concat + FC. ⊳ Selection strategy: High loss samples. | ⊳ Weather1 [107]: accuracy = 98.80% ⊳ Weather2 [108]: F1 score = 0.872 ⊳ Proposed dataset [106]: F1 score = 0.772 |
3D object detection [109] | ⊳ The first work that introduced active learning into 3D object detection in autonomous driving. | ⊳ 3D Detector: VoxelNet ⊳ Score-function: Diversity: | ⊳ nuScenes [110]: mAP = 45.02. |
Lane detection [111] | ⊳ The first work that introduced active learning into lane detection in autonomous driving. | ⊳ Student model: ResNet-122 (for PLN [112]) ResNet-18 (for UFLD [113]) ⊳ Teacher model: SENet-154 (for PLN [112]) ResNet-101 (for UFLD [113]) ⊳ Score-function: Combined the uncertainty and diversity metrics. | ⊳ CULane [114] and LLAMAS [115]. (F1 score not reported) |
Crowd counting [116] | ⊳ The first work that used predictive uncertainty for sample selection pertaining to crowd counting task. | ⊳ Local feature block: VGG16 ⊳ Non-local feature block: Transformer ⊳ Score-function: Informativeness difference: | ⊳ UCF-QNRF [117]: MAE = 86; MSE = 146. ⊳ UCF CC [118]: MAE = 210; MSE = 305.4. ⊳ ShanghaiTech-A [119]: MAE = 61.5; MSE = 103.4. ⊳ ShanghaiTech-B [119]: MAE = 7.5; MSE = 11.9. ⊳ NWPU [120]: MAE = 78; MSE = 448. |
Crowd counting [121] | ⊳ Proposed a partition-based sample selection with weights (PSSW) strategy to actively select and annotate both diverse and dissimilar samples for network training. | ⊳ Backbone: VGG16 pretrained by imagenet ⊳ Score-function: Diverse in density and dissimilar to previous selections. | ⊳ ShanghaiTech-A [119]: MAE = 80.4; MSE = 138.8. ⊳ ShanghaiTech-B [119]: MAE = 12.7; MSE = 20.4. ⊳ UCF CC [118]: MAE = 318.7; MSE = 421.6. ⊳ Mall [122]: MAE = 3.8; MSE = 5.4. ⊳ TRANCOS [123]: MAE = 7.5. ⊳ DCC [124]: MAE = 4.5. |
Applications | Comments | Implementation | Evaluation |
---|---|---|---|
Medical image detection and classification [22,26] | ⊳ Combined active learning, incremental fine-tuning, and transfer learning. | ⊳ Network: AlexNet pretrained by imagenet ⊳ Selection strategy: Entropy: Diversity: | ⊳ polyp detection: ↓ 86% labels. ⊳ pulmonary embolism detection: ↓ 80% labels. ⊳ colonoscopy frame classification: ↓ 82% labels. ⊳ scene classification: ↓ 35% labels. |
COVID-19 Lung Ultrasound Multi-symptom Classification [131] | ⊳ The first work that introduced active learning into ultrasound classification for COVID-19-assisted diagnosis. | ⊳ Backbone: ResNet50 pretrained by imagenet ⊳ Score-function: Least confidence: t Multi-label entropy: | ⊳ COVID19-LUSMS v1: ↓ 80% labels. |
Brain tumor Classification [132] | ⊳ Sampling candidates by discarding subsets of training samples with the highest and lowest uncertainty scores. | ⊳ Network: AlexNet pretrained by imagenet ⊳ Score-function: Combined entropy and Kullback–Leibler (KL) divergence: | ⊳ MICCAI BRATS [133,134,135]: ↓ 40% labels. |
Diabetic retinopathy classification [136] | ⊳ The first work that introduced active learning into lane detection in autonomous driving. | ⊳ Bayesian convolutional neural network (BCNN): Monte-Carlo drop-out ⊳ Teacher model: SENet-154 (for PLN [112]) ResNet-101 (for UFLD [113]) ⊳ Score-function: entropy. | ⊳ APTOS 2019 [137]: AUC = 0.99 (multi-class classification) Accuracy = 92% (multi-class classification) Accuracy = 85% (BCNN in Active Learning) |
Histo-pathology image analysis [138] | ⊳ The first work that proposed an AL framework (PathAL) to dynamically identify important samples to annotate and to distinguish noisy from hard samples in the training set. | ⊳ Backbone: EfficientNet-B0 [139] ⊳ Noisy sample detector: O2U-Net [140] Curriculum Sample Classification: CurriculumNet [141] ⊳ Score-function: Distinguished noisy samples from hard ones, and selected the most informative samples to be annotated. | ⊳ PANDA [142]: quadratic weighted kappa = 89.5. |
Gastric adenocarcinoma and colorectal cancer [143] | ⊳ The first work that explored the identification of the most informative region of patches and proposed a patch location system to select patches. | ⊳ Backbone: ResNet-18 ⊳ Loss-prediction module [52]: 4 × (GAP + FC + ReLU) + Concat + FC. ⊳ Score-function: | ⊳ TCGA [144,145]: AUC = 0.933. accuracy = 92.7%. |
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Wu, M.; Li, C.; Yao, Z. Deep Active Learning for Computer Vision Tasks: Methodologies, Applications, and Challenges. Appl. Sci. 2022, 12, 8103. https://doi.org/10.3390/app12168103
Wu M, Li C, Yao Z. Deep Active Learning for Computer Vision Tasks: Methodologies, Applications, and Challenges. Applied Sciences. 2022; 12(16):8103. https://doi.org/10.3390/app12168103
Chicago/Turabian StyleWu, Mingfei, Chen Li, and Zehuan Yao. 2022. "Deep Active Learning for Computer Vision Tasks: Methodologies, Applications, and Challenges" Applied Sciences 12, no. 16: 8103. https://doi.org/10.3390/app12168103