A comprehensive survey of continual learning: theory, method and application
To cope with real-world dynamics, an intelligent system needs to incrementally acquire,
update, accumulate, and exploit knowledge throughout its lifetime. This ability, known as …
update, accumulate, and exploit knowledge throughout its lifetime. This ability, known as …
Biological underpinnings for lifelong learning machines
Biological organisms learn from interactions with their environment throughout their lifetime.
For artificial systems to successfully act and adapt in the real world, it is desirable to similarly …
For artificial systems to successfully act and adapt in the real world, it is desirable to similarly …
imap: Implicit map** and positioning in real-time
We show for the first time that a multilayer perceptron (MLP) can serve as the only scene
representation in a real-time SLAM system for a handheld RGB-D camera. Our network is …
representation in a real-time SLAM system for a handheld RGB-D camera. Our network is …
Class-incremental learning: A survey
Deep models, eg, CNNs and Vision Transformers, have achieved impressive achievements
in many vision tasks in the closed world. However, novel classes emerge from time to time in …
in many vision tasks in the closed world. However, novel classes emerge from time to time in …
Ditto: Fair and robust federated learning through personalization
Fairness and robustness are two important concerns for federated learning systems. In this
work, we identify that robustness to data and model poisoning attacks and fairness …
work, we identify that robustness to data and model poisoning attacks and fairness …
Class-incremental learning: survey and performance evaluation on image classification
For future learning systems, incremental learning is desirable because it allows for: efficient
resource usage by eliminating the need to retrain from scratch at the arrival of new data; …
resource usage by eliminating the need to retrain from scratch at the arrival of new data; …