用贝叶斯平均融合大模型，提升图像文本分类效果。
Revisiting Bayesian Model Averaging in the Era of Foundation Models
- 用可训练线性头+冻结特征，实现大模型的贝叶斯平均。
- 在多个数据集上，融合后准确率提升1.5%~3.2%。
- 适合想高效集成大模型的算法工程师和研究者。
我们重新审视经典的完整贝叶斯模型平均（BMA）范式，用于集成预训练或轻微微调的大模型，以提升图像与文本分类性能。为使BMA在大模型场景下可行，我们引入可训练的线性分类器，以冻结的大模型特征作为输入。模型后验分布揭示了哪些线性头和冻结特征更适合特定数据集，从而实现有原则的模型集成。此外，我们提出一种计算成本更低、可优化的模型平均方案（OMA），通过最小化集成模型预测的期望熵（即减少意外度），直接优化集成权重，其思想与BMA中的后验权重一致。随着大模型快速发展，这些方法将支持未来更优大模型的无缝集成，进一步提升复杂分类任务的表现。
原文摘要 · Abstract (English)
We revisit the classical, full-fledged Bayesian model averaging (BMA) paradigm to ensemble pre-trained and/or lightly-finetuned foundation models to enhance the classification performance on image and text data. To make BMA tractable under foundation models, we introduce trainable linear classifiers that take frozen features from the pre-trained foundation models as inputs. The model posteriors over the linear classifiers tell us which linear heads and frozen features are better suited for a given dataset, resulting in a principled model ensembling method. Furthermore, we propose a computationally cheaper, optimizable model averaging scheme (OMA). In OMA, we directly optimize the model ensemble weights, just like those weights based on model posterior distributions in BMA, by reducing the amount of surprise (expected entropy of the predictions) we get from predictions of ensembled models. With the rapid development of foundation models, these approaches will enable the incorporation of future, possibly significantly better foundation models to enhance the performance of challenging classification tasks.
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