Composing Ensembles of Pre-trained Models via Iterative Consensus

• URL: http://arxiv.org/abs/2210.11522v1
• Date: Thu, 20 Oct 2022 18:46:31 GMT
• Title: Composing Ensembles of Pre-trained Models via Iterative Consensus
• Authors: Shuang Li, Yilun Du, Joshua B. Tenenbaum, Antonio Torralba, Igor Mordatch
• Abstract summary: We propose a unified framework for composing ensembles of different pre-trained models. We use pre-trained models as "generators" or "scorers" and compose them via closed-loop iterative consensus optimization. We demonstrate that consensus achieved by an ensemble of scorers outperforms the feedback of a single scorer.
• Score: 95.10641301155232
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large pre-trained models exhibit distinct and complementary capabilities dependent on the data they are trained on. Language models such as GPT-3 are capable of textual reasoning but cannot understand visual information, while vision models such as DALL-E can generate photorealistic photos but fail to understand complex language descriptions. In this work, we propose a unified framework for composing ensembles of different pre-trained models -- combining the strengths of each individual model to solve various multimodal problems in a zero-shot manner. We use pre-trained models as "generators" or "scorers" and compose them via closed-loop iterative consensus optimization. The generator constructs proposals and the scorers iteratively provide feedback to refine the generated result. Such closed-loop communication enables models to correct errors caused by other models, significantly boosting performance on downstream tasks, e.g. improving accuracy on grade school math problems by 7.5%, without requiring any model finetuning. We demonstrate that consensus achieved by an ensemble of scorers outperforms the feedback of a single scorer, by leveraging the strengths of each expert model. Results show that the proposed method can be used as a general purpose framework for a wide range of zero-shot multimodal tasks, such as image generation, video question answering, mathematical reasoning, and robotic manipulation. Project page: https://energy-based-model.github.io/composing-pretrained-models.

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