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Poster
Model Ratatouille: Recycling Diverse Models for Out-of-Distribution Generalization
Alexandre Rame · Kartik Ahuja · Jianyu Zhang · Matthieu Cord · Leon Bottou · David Lopez-Paz

Thu Jul 27 01:30 PM -- 03:00 PM (PDT) @ Exhibit Hall 1 #208
in Poster Session 5 »

Foundation models are redefining how AI systems are built. Practitioners now follow a standard procedure to build their machine learning solutions: from a pre-trained foundation model, they fine-tune the weights on the target task of interest. So, the Internet is swarmed by a handful of foundation models fine-tuned on many diverse tasks: these individual fine-tunings exist in isolation without benefiting from each other. In our opinion, this is a missed opportunity, as these specialized models contain rich and diverse features. In this paper, we thus propose model ratatouille, a new strategy to recycle the multiple fine-tunings of the same foundation model on diverse auxiliary tasks. Specifically, we repurpose these auxiliary weights as initializations for multiple parallel fine-tunings on the target task; then, we average all fine-tuned weights to obtain the final model. This recycling strategy aims at maximizing the diversity in weights by leveraging the diversity in auxiliary tasks. Empirically, it improves the state of the art on the reference DomainBed benchmark for out-of-distribution generalization. Looking forward, this work contributes to the emerging paradigm of updatable machine learning where, akin to open-source software development, the community collaborates to reliably update machine learning models.

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Author Information

Alexandre Rame (Sorbonne University)

I am a PhD student at Sorbonne University in Paris under the supervision of Professor Matthieu Cord. My thesis aims at improving the robustness of deep learning networks by leveraging their diversity.

Kartik Ahuja (FAIR (Meta AI))
Jianyu Zhang (New York University)
Matthieu Cord (Sorbonne University)
Leon Bottou (Meta AI)
David Lopez-Paz (Facebook AI Research)

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