Popular zero-shot models suffer due to artifacts inherited from pretraining. A particularly detrimental artifact, caused by unbalanced web-scale pretraining data, is mismatched label distribution. Existing approaches that seek to repair the label distribution are not suitable in zero-shot settings, as they have incompatible requirements such as access to labeled downstream task data or knowledge of the true label balance in the pretraining distribution. We sidestep these challenges and introduce a simple and lightweight approach to adjust pretrained model predictions via optimal transport. Our technique requires only an estimate of the label distribution of a downstream task. Theoretically, we characterize the improvement produced by our procedure under certain mild conditions and provide bounds on the error caused by misspecification. Empirically, we validate our method in a wide array of zero-shot image and text classification tasks, improving accuracy by 4.8% and 15.9% on average, and beating baselines like Prior Matching -- often by significant margins -- in 17 out of 21 datasets.

零样本模型在预训练时遗传了一些问题，其中一个特别有害的问题是由于不平衡的网络规模预训练数据导致的标签分布不匹配。我们引入了一种简单且轻量级的方法，通过最优传输来调整预训练模型的预测结果，在广泛的零样本图像和文本分类任务中实验证实了我们的方法能够在准确率上提高4.8%和15.9%的平均值，并在21个数据集中的17个中击败了类似Prior Matching的基线方法。

OTTER: 通过最优传输提高零-shot分类