arXiv:2510.22480v1 Announce Type: new 
Abstract: Knowledge Distillation (KD) aims to train a lightweight student model by transferring knowledge from a large, high-capacity teacher. Recent studies have shown that leveraging diverse teacher perspectives can significantly improve distillation performance; however, achieving such diversity typically requires multiple teacher networks, leading to high computational costs. In this work, we propose a novel cost-efficient knowledge augmentation method for KD that generates diverse multi-views by attaching multiple branches to a single teacher. To ensure meaningful semantic variation across multi-views, we introduce two angular diversity objectives: 1) constrained inter-angle diversify loss, which maximizes angles between augmented views while preserving proximity to the original teacher output, and 2) intra-angle diversify loss, which encourages an even distribution of views around the original output. The ensembled knowledge from these angularly diverse views, along with the original teacher, is distilled into the student. We further theoretically demonstrate that our objectives increase the diversity among ensemble members and thereby reduce the upper bound of the ensemble's expected loss, leading to more effective distillation. Experimental results show that our method surpasses an existing knowledge augmentation method across diverse configurations. Moreover, the proposed method is compatible with other KD frameworks in a plug-and-play fashion, providing consistent improvements in generalization performance.

تقدم دراسة جديدة طريقة مبتكرة لتقطير المعرفة تعزز تدريب نماذج الطلاب الخفيفة من خلال استخدام وجهات نظر متنوعة من نموذج معلم واحد. لا تحسن هذه الطريقة الأداء فحسب، بل تقلل أيضًا من التكاليف الحاسوبية المرتبطة عادةً باستخدام عدة شبكات معلم. هذه الخطوة مهمة لأنها تجعل تقطير المعرفة أكثر سهولة وكفاءة، مما قد يفيد تطبيقات مختلفة في تعلم الآلة.

Un nuevo estudio presenta un método innovador para la destilación de conocimientos que mejora la formación de modelos estudiantes ligeros al utilizar perspectivas diversas de un solo modelo docente. Este enfoque no solo mejora el rendimiento, sino que también reduce los costos computacionales típicamente asociados con el uso de múltiples redes docentes. Este avance es significativo, ya que hace que la destilación de conocimientos sea más accesible y eficiente, lo que podría beneficiar a diversas aplicaciones en aprendizaje automático.

Une nouvelle étude présente une méthode innovante pour la distillation des connaissances qui améliore la formation de modèles étudiants légers en utilisant des perspectives diverses d'un seul modèle enseignant. Cette approche améliore non seulement les performances, mais réduit également les coûts computationnels généralement associés à l'utilisation de plusieurs réseaux enseignants. Cette avancée est significative car elle rend la distillation des connaissances plus accessible et efficace, ce qui pourrait bénéficier à diverses applications en apprentissage automatique.

A new study introduces an innovative method for knowledge distillation that enhances the training of lightweight student models by utilizing diverse perspectives from a single teacher model. This approach not only improves performance but also reduces the computational costs typically associated with using multiple teacher networks. This advancement is significant as it makes knowledge distillation more accessible and efficient, potentially benefiting various applications in machine learning.

Single-Teacher View Augmentation: Boosting Knowledge Distillation via Angular Diversity

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Single-Teacher View Augmentation: Boosting Knowledge Distillation via Angular Diversity

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