Continual Learning for Real-World Autonomous Systems: Algorithms, Challenges and Frameworks
By Khadija Shaheen, Muhammad Abdullah Hanif, Osman Hasan, Muhammad Shafique. 2021
150 citations recorded by the source corpus.
Continual learning is essential for all real-world applications, as frozen pre-trained models cannot effectively deal with non-stationary data distributions. The purpose of this study is to review the state-of-the-art methods that allow continuous learning of computational models over time. We primarily focus on the learning algorithms that perform continuous learning in an online fashion from considerably large (or infinite) sequential data and require substantially low computational and memory resources. We critically analyze the key challenges associated with continual learning for autonomous real-world systems and compare current methods in terms of computations, memory, and network/model complexity. We also briefly describe the implementations of continuous learning algorithms under three main autonomous systems, i.e., self-driving vehicles, unmanned aerial vehicles, and urban robots. The learning methods of these autonomous systems and their strengths and limitations are extensively explored in this article.
Subfields: Artificial Intelligence, Infectious Diseases
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Matteo Boschini
- Lorenzo Bonicelli
- Simone Calderara
- Concetto Spampinato
- Angelo Porrello
- Simone Palazzo
- Giovanni Bellitto
- Matteo Pennisi
In Infectious Diseases
Citations in the corpus
- The Ideal Continual Learner: An Agent That Never Forgets (2023, neutral): “…Ramesh & Chaudhari, 2022; Douillard et al., 2022), or combinations thereof (Yan et al., 2021; Xie et al., 2022; Wang et al., 2022b; Frascaroli et al., 2023); for surveys, see, e.g., Parisi et al. (2019); De Lange et al. (2022); Qu et al. (2021); Shaheen et al. (2022); van de Ven et al. (2022).”
- Transfer without Forgetting (2022, background): “However, the desiderata of practical applications [59] often overstep the boundaries of the typical i.”
- Three types of incremental learning (2022, unknown): “In recent years, this area of machine learning research has been rapidly expanding, fuelled by the potential utility of deploying continual learning algorithms for applications such as medical diagnosis 6 , autonomous driving 7 or predicting financial markets 8 .”