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This paper proposes \textit{Self-Supervised Continual Learning (SCL)} for regularization-based class incremental learning. The novel pretext task in SCL utilizes randomly-transformed labels without depending on data-augmented transforms. \textit{SCL} trained with a novel incremental task-regularizer and an orthogonal weight modifications backbone shows promising performance on three datasets.