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Banafsheh Karimian

Alumni

Publications

Learning Task-Agnostic Representations through Multi-Teacher Distillation
Philippe Formont
Maxime Darrin
Banafsheh Karimian
Eric Granger
Jackie Cheung
Ismail Ben Ayed
Mohammadhadi Shateri
Pablo Piantanida
Casting complex inputs into tractable representations is a critical step across various fields. Diverse embedding models emerge from differe… (see more)nces 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.
2025-09-17
NeurIPS.cc/2025/Conference (poster)
doi.org
openreview.net