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Continual learning refers to the problem where the training data is availablein sequential chunks, termed "tasks". The majority of progress in continuallearning has been stunted by the problem of catastrophic forgetting, which iscaused by sequential training of the model on streams of data. Moreover, itbecomes computationally expensive to sequentially train large models multipletimes. To mitigate both of these problems at once, we propose a novel method tocontinually train transformer-based vision models using low-rank adaptation andtask arithmetic. Our method completely bypasses the problem of catastrophicforgetting, as well as reducing the computational requirement for trainingmodels on each task. When aided with a small memory of 10 samples per class,our method achieves performance close to full-set finetuning. We presentrigorous ablations to support the prowess of our method.

Task Arithmetic with LoRA for Continual Learning