Active Learning Use-Case in Computer Vision: Autonomous Driving
Abstract Active learning is a crucial technique in deep learning that helps optimize data annotation
by selectively labeling the most informative samples. In the domain of autonomous driving, where
vast amounts of image and sensor data are collected, active learning significantly reduces labeling
costs while maintaining high model performance. This paper discusses the application of active
learning in autonomous driving, emphasizing its role in improving object detection, semantic
segmentation, and decision-making models. Additionally, challenges and future directions for active
learning in autonomous driving are explored.
Introduction Autonomous driving relies on deep learning models trained on massive datasets to
recognize and interpret road scenes accurately. However, labeling such large-scale datasets is costly
and time-consuming. Active learning mitigates this challenge by selecting the most uncertain and
informative samples for annotation, thereby optimizing the training process. This approach enhances
the efficiency of autonomous driving models by improving their performance with minimal labeled
data. This paper presents a comprehensive analysis of active learning techniques in autonomous
driving, their advantages, and the associated challenges.
Literature Review Active learning has been widely studied and implemented in various computer
vision tasks. Several key strategies exist, including uncertainty sampling, query-by-committee, and
diversity-based sampling.
1. Uncertainty Sampling: Studies show that selecting images where the model exhibits high
uncertainty improves object detection performance (e.g., identifying pedestrians, vehicles,
and road signs).
2. Query-by-Committee: Techniques where multiple models evaluate an image's importance
before selection have been applied to improve semantic segmentation in self-driving
datasets.
3. Diversity-Based Sampling: Ensuring a diverse set of samples prevents redundancy and
enhances learning efficiency.
Notable research, such as works on Tesla's autopilot system and Waymo’s self-driving cars,
demonstrates the effectiveness of active learning in improving model robustness while minimizing
human annotation efforts. Despite these advancements, challenges such as class imbalance, dataset
bias, and computational overhead remain.
Conclusion Active learning plays a vital role in optimizing data labeling for autonomous driving
systems. By selecting the most informative samples, it reduces annotation costs while maintaining
high accuracy. Although challenges exist, future advancements in uncertainty estimation and active
learning frameworks are expected to further enhance self-driving technology. This research
highlights the necessity for continuous improvements in active learning methodologies to ensure
safer and more efficient autonomous driving systems.
Active Learning Use-Case in the Medical Domain
Abstract Medical image analysis benefits significantly from active learning by reducing annotation
costs and improving model efficiency. Since medical data labeling requires expert knowledge, active
learning optimizes the annotation process by selecting the most valuable samples for manual
labeling. This paper explores the application of active learning in disease diagnosis, segmentation of
medical images, and anomaly detection, highlighting its advantages and limitations.
Introduction Medical imaging is a critical component of modern healthcare, assisting in disease
detection, diagnosis, and treatment planning. However, deep learning models for medical imaging
require large annotated datasets, which are costly to obtain due to the need for expert radiologists.
Active learning addresses this challenge by selectively choosing the most uncertain and informative
samples for annotation, reducing workload while maintaining model accuracy. This paper reviews the
applications of active learning in medical imaging and its potential to revolutionize automated
diagnosis systems.
Literature Review Several active learning techniques have been successfully applied in medical
imaging:
1. Uncertainty Sampling: Used in tasks like tumor detection, where the model prioritizes
ambiguous regions for expert annotation.
2. Query-by-Committee: Applied in medical segmentation to improve the accuracy of organ
and lesion detection.
3. Deep Bayesian Active Learning: Studies have demonstrated its effectiveness in improving
disease classification models, such as detecting pneumonia from chest X-rays.
4. Hybrid Methods: Combining uncertainty sampling with diversity-based approaches ensures
better generalization in diagnostic models.
Recent research shows that active learning significantly enhances the performance of medical AI
models while reducing annotation costs. However, challenges such as inter-expert variability and the
need for extensive computational resources persist.
Conclusion Active learning has proven to be a powerful tool in medical image analysis, optimizing
annotation efforts and improving diagnostic accuracy. Despite existing challenges, its potential to
reduce costs and enhance model performance makes it a promising approach for medical AI
applications. Future research should focus on refining active learning strategies to address domain-
specific issues and improve the reliability of automated diagnosis systems.
