… related to text classification in increment learning. Our proposed model, Learn#, uses the
novel incremental learning to … learning strategy to select the correct prediction results of the …

… predictions, where σ indicates the softmax function. After training on task t, we evaluate the
performance … with previous classes to improve performance based on the assumption that …

… incremental learning algorithms and analyze them according to these properties, (2) introduce
a unified formalization of the class-incremental learning … number of incremental states, (4) …

… further improves the performance based on them. However, for performance reasons, some
… phase, the outputs of HC or mPFC were decided for prediction. FearNet has good memory …

… To help the student model also learn the “reason” for the predictions produced by the
teacher model, we propose a novel EBKD loss for ASR incremental learning to train the student …

… incremental learning, which utilizes both the rehearsal strategy and the distillation strategy.
Let us first formulate class incremental learning. … can effectively alleviate the prediction bias. …

… -based incremental learning methods treat the old and new models as the teacher and student…
KD takes the form of crossentropy loss, with softened predictions of the previous model as …

… We present a novel class incremental learning approach based on deep neural networks,
which continually learns new tasks with limited memory for storing examples in the previous …

… building a model for predicting student’s performance. The results using … incremental
ensemble of classifiers as a technique for predicting students’ performance in distance education …

… incremental learning paradigm called Deep Model Consolidation (DMC), which works well
even when the original training … In this way, we encourage the predicted bounding box of the …