Rethinking the Bias of Foundation Model under Long-tailed Distribution
Keywords: foundation model, long-tailed learning
TL;DR: We propose a novel backdoor adjustment to solve imbalance problem.
Abstract: Long-tailed learning has garnered increasing attention due to its practical significance. Among the various approaches, the fine-tuning paradigm has gained considerable interest with the advent of foundation models. However, most existing methods primarily focus on leveraging knowledge from these models, overlooking the inherent biases introduced by the imbalanced training data they rely on. In this paper, we examine how such imbalances affect long-tailed downstream tasks. Specifically, we refer to the biases in foundation models and downstream tasks as parameter imbalance and data imbalance, respectively. Through fine-tuning, we observe that parameter imbalance plays a more critical role, while data imbalance can be mitigated using existing re-balancing strategies. Moreover, we find that parameter imbalance cannot be effectively addressed by current re-balancing techniques, such as adjusting the logits, during training, unlike data imbalance. To tackle both imbalances simultaneously, we constitute a causal structure graph and view the partial semantic factor as the confounder, which brings spurious correlations between input samples and labels. To resolve the negative effects of this, we propose a novel backdoor adjustment method that learns the true causal effect between input samples and labels, rather than merely fitting the correlations in the data. Experimental results validate the effectiveness of our method.
Primary Area: applications to computer vision, audio, language, and other modalities
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Submission Number: 5929
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