일반적인 머신러닝 방식은 충분한 레이블 데이터를 이용해 모델을 학습시킨 후, 그 모델을 이용하여 예측을 수행하는 것이다. 만약 학습에 필요한 데이터가 추가로 생기는 경우, 모델을 재학습 시켜야 한다. 연속학습은 이렇게 학습해야 할 데이터가 계속 추가되는 경우 효율적으로 학습하는 과정을 말한다. 그런데 추가된 데이터를 이용해 모델을 재학습하는 경우 성능 저하가 발생할 수 있다. 그래서 기존 데이터에 추가 데이터를 합친 다음, 모델을 초기화하고 다시 학습 시켜야 한다. 이러한 과정은 많은 연산량과 학습 시간을 요구한다. 본 논문은, 이전 학습 단계 중 중간 정도 학습된 모델을 이용하여 추가학습을 진행하도록 하여, 성능과 학습 시간을 개선하는 방법을 제시한다. MNIST 데이터셋과 CNN을 이용한 실험을 통하여 제안하는 방법의 효용성을 확인하였다.
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