… To handle the vast amount of continuous flowing data, the important concept of incremental
learning, use of ensemble technique for incremental learning, transforming knowledge and …

… In a class-incremental learning paradigm, we aim to incrementally adapt a unified model
to new obtained classes. For a dataset, we define a set of tasks T1,T2,...,Tt, where Tt …

… new knowledge introduces with the new data. In this paper, the adaptive incremental learning
… is proposed, which competently transforms the knowledge throughout the system and also …

… Incremental learning enables artificial agents to learn from … deep neural networks, incremental
learning remains very challenging… We tackle classincremental learning without memory by …

… The representation of the user's knowledge and skills is done as a modified subset of …
knowledge. Compared to the system's expert knowledge, some of the concepts and transformation …

… methodology [48], [49], we propose an adaptive incremental learning framework to enable
knowledge accumulation and transformation to benefit learning from continuous data stream. …

… In continual learning, within the class-incremental learning scenario, we experience a stream
… ) and those to be learned by the students are first transformed into a joint space. Later on, …

… dilemma in class-incremental learning where no exemplars … between learning new information
and maintaining old knowledge, … transformations in SPB-I and SPB-M, our goal is to learn …

… We propose a novel approach to address the task incremental learning problem for both the
… -based Knowledge Retention (RKR), applies weight rectifications and affine transformations …

… (NECIL) requires deep models to maintain existing knowledge while continuously learning
… incremental learning. However, since the model continuously learns new knowledge, the …