Learning Task-Agnostic Representations through Multi-Teacher Distillation

• URL: http://arxiv.org/abs/2510.18680v1
• Date: Tue, 21 Oct 2025 14:36:33 GMT
• Title: Learning Task-Agnostic Representations through Multi-Teacher Distillation
• Authors: Philippe Formont, Maxime Darrin, Banafsheh Karimian, Jackie CK Cheung, Eric Granger, Ismail Ben Ayed, Mohammadhadi Shateri, Pablo Piantanida,
• Abstract summary: We introduce a task-agnostic framework based on a majority vote" objective function.<n>We demonstrate that this function is bounded by the mutual information between student and teachers' embeddings.<n>Our method effectively leverages teacher diversity, resulting in representations enabling better performance for a wide range of downstream tasks.
• Score: 59.488314181423284
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Casting complex inputs into tractable representations is a critical step across various fields. Diverse embedding models emerge from differences in architectures, loss functions, input modalities and datasets, each capturing unique aspects of the input. Multi-teacher distillation leverages this diversity to enrich representations but often remains tailored to specific tasks. In this paper, we introduce a task-agnostic framework based on a ``majority vote" objective function. We demonstrate that this function is bounded by the mutual information between student and teachers' embeddings, leading to a task-agnostic distillation loss that eliminates dependence on task-specific labels or prior knowledge. Our evaluations across text, vision models, and molecular modeling show that our method effectively leverages teacher diversity, resulting in representations enabling better performance for a wide range of downstream tasks such as classification, clustering, or regression. Additionally, we train and release state-of-the-art embedding models, enhancing downstream performance in various modalities.

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