Pre-trained foundation models, owing primarily to their enormous capacity and exposure to vast amount of training data scraped from the internet, enjoy the advantage of storing knowledge about plenty of real-world concepts. Such models are typically fine-tuned on downstream datasets to produce remarkable state-of-the-art performances. While various fine-tuning methods have been devised and are shown to be highly effective, we observe that a fine-tuned model’s ability to recognize concepts on tasks different from the downstream one is reduced significantly compared to its pre-trained counterpart. This is clearly undesirable as a huge amount of time and money went into learning those very concepts in the first place. We call this undesirable phenomenon “concept forgetting” and via experiments show that most end-to-end fine-tuning approaches suffer heavily from this side effect. To this end, we also propose a rather simple fix to this problem by designing a method called LDIFS (short for l2 distance in feature space) that simply preserves the features of the original foundation model during fine-tuning. We show that LDIFS significantly reduces concept forgetting without having noticeable impact on the downstream task performance.
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