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Class incremental learning refers to a special multi-class classificationtask, in which the number of classes is not fixed but is increasing with thecontinual arrival of new data. Existing researches mainly focused on solvingcatastrophic forgetting problem in class incremental learning. To this end,however, these models still require the old classes cached in the auxiliarydata structure or models, which is inefficient in space or time. In this paper,it is the first time to discuss the difficulty without support of old classesin class incremental learning, which is called as softmax suppression problem.To address these challenges, we develop a new model named Label Mapping withResponse Consolidation (LMRC), which need not access the old classes anymore.We propose the Label Mapping algorithm combined with the multi-head neuralnetwork for mitigating the softmax suppression problem, and propose theResponse Consolidation method to overcome the catastrophic forgetting problem.Experimental results on the benchmark datasets show that our proposed methodachieves much better performance compared to the related methods in differentscenarios.

Label Mapping Neural Networks with Response Consolidation for Class Incremental Learning