310 papers with code • 21 benchmarks • 9 datasets
Incremental learning aims to develop artificially intelligent systems that can continuously learn to address new tasks from new data while preserving knowledge learned from previously learned tasks.
A major open problem on the road to artificial intelligence is the development of incrementally learning systems that learn about more and more concepts over time from a stream of data.
Detecting test samples drawn sufficiently far away from the training distribution statistically or adversarially is a fundamental requirement for deploying a good classifier in many real-world machine learning applications.
Despite their success for object detection, convolutional neural networks are ill-equipped for incremental learning, i. e., adapting the original model trained on a set of classes to additionally detect objects of new classes, in the absence of the initial training data.