arXiv:2502.04499v2 Announce Type: replace 
Abstract: Knowledge distillation (KD) is a popular method of transferring knowledge from a large "teacher" model to a small "student" model. Previous work has explored various layer-selection strategies (e.g., forward matching and in-order random matching) for intermediate-layer matching in KD, where a student layer is forced to resemble a certain teacher layer. In this work, we revisit such layer-selection strategies and observe an intriguing phenomenon that layer-selection strategy does not matter (much) in intermediate-layer matching -- even seemingly nonsensical matching strategies such as reverse matching still result in surprisingly good student performance. We provide an interpretation for this phenomenon by examining the angles between teacher layers viewed from the student's perspective. Our work sheds light on KD practice, as layer-selection strategies may not be the main focus of KD system design, and vanilla forward matching works well in most setups.

دراسة حديثة تعيد تقييم استراتيجيات اختيار الطبقات في تقنيات نقل المعرفة (KD)، كاشفة أن اختيار الاستراتيجية له تأثير ضئيل على أداء نموذج الطالب. تشير الأبحاث إلى أن حتى طرق المطابقة غير التقليدية، مثل المطابقة العكسية، يمكن أن تؤدي إلى نتائج فعالة بشكل مدهش. هذا يتحدى الافتراضات السابقة حول أهمية تقنيات اختيار الطبقات المحددة في KD.

Un estudio reciente revisita las estrategias de selección de capas en la destilación de conocimiento (KD), revelando que la elección de la estrategia tiene un impacto mínimo en el rendimiento del modelo estudiante. La investigación indica que incluso métodos de emparejamiento no convencionales, como el emparejamiento inverso, pueden producir resultados sorprendentemente efectivos. Esto desafía suposiciones previas sobre la importancia de técnicas específicas de selección de capas en la KD.

Une étude récente réévalue les stratégies de sélection de couches dans la distillation de connaissances (KD), révélant que le choix de la stratégie a un impact minimal sur la performance des modèles étudiants. La recherche indique que même des méthodes de correspondance non conventionnelles, telles que la correspondance inversée, peuvent donner des résultats étonnamment efficaces. Cela remet en question les hypothèses précédentes sur l'importance des techniques de sélection de couches spécifiques dans la KD.

A recent study revisits layer-selection strategies in Knowledge Distillation (KD), revealing that the choice of strategy has minimal impact on student model performance. The research indicates that even unconventional matching methods, such as reverse matching, can yield surprisingly effective results. This challenges previous assumptions about the importance of specific layer-selection techniques in KD.

Revisiting Intermediate-Layer Matching in Knowledge Distillation: Layer-Selection Strategy Doesn't Matter (Much)

arXiv:2601.08033v1 Announce Type: new 
Abstract: Graph Neural Networks (GNNs) are the go-to model for graph data analysis. However, GNNs rely on two key operations - aggregation and update, which can pose challenges for low-latency inference tasks or resource-constrained scenarios. Simple Multi-Layer Perceptrons (MLPs) offer a computationally efficient alternative. Yet, training an MLP in a supervised setting often leads to suboptimal performance. Knowledge Distillation (KD) from a GNN teacher to an MLP student has emerged to bridge this gap. However, most KD methods either transfer knowledge uniformly across all nodes or rely on graph-agnostic indicators such as prediction uncertainty. We argue this overlooks a more fundamental, graph-centric inquiry: "How important is a node to the structure of the graph?" We introduce a framework, InfGraND, an Influence-guided Graph KNowledge Distillation from GNN to MLP that addresses this by identifying and prioritizing structurally influential nodes to guide the distillation process, ensuring that the MLP learns from the most critical parts of the graph. Additionally, InfGraND embeds structural awareness in MLPs through one-time multi-hop neighborhood feature pre-computation, which enriches the student MLP's input and thus avoids inference-time overhead. Our rigorous evaluation in transductive and inductive settings across seven homophilic graph benchmark datasets shows InfGraND consistently outperforms prior GNN to MLP KD methods, demonstrating its practicality for numerous latency-critical applications in real-world settings.

تم تقديم إطار جديد يسمى InfGraND لتسهيل تقطير المعرفة الموجه من الشبكات العصبية الرسومية (GNN) إلى الشبكات متعددة الطبقات (MLP). يهدف هذا الإطار إلى تحسين كفاءة MLP من خلال إعطاء الأولوية للعقد الهيكلية المؤثرة في الرسم البياني، مما يعالج التحديات التي تواجهها GNN التقليدية في البيئات ذات الكمون المنخفض والموارد المحدودة.

Se ha introducido un nuevo marco llamado InfGraND para facilitar la distilación de conocimiento guiada por la influencia de las Redes Neuronales de Grafos (GNN) hacia los Perceptrones Multicapa (MLP). Este marco tiene como objetivo mejorar la eficiencia de los MLP priorizando los nodos estructuralmente influyentes en el grafo, abordando así los desafíos que enfrentan las GNN tradicionales en entornos de baja latencia y con recursos limitados.

Un nouveau cadre nommé InfGraND a été introduit pour faciliter la distillation de connaissances guidée par l'influence des réseaux de neurones graphiques (GNN) vers les perceptrons multicouches (MLP). Ce cadre vise à améliorer l'efficacité des MLP en priorisant les nœuds structurellement influents dans le graphe, abordant ainsi les défis rencontrés par les GNN traditionnels dans des environnements à faible latence et à ressources limitées.

A new framework named InfGraND has been introduced to facilitate Influence-guided Knowledge Distillation from Graph Neural Networks (GNNs) to Multi-Layer Perceptrons (MLPs). This framework aims to enhance the efficiency of MLPs by prioritizing structurally influential nodes in the graph, addressing challenges faced by traditional GNNs in low-latency and resource-constrained environments.

InfGraND: An Influence-Guided GNN-to-MLP Knowledge Distillation

One More Thing in AI – Your Shortcut to AI Mastery

Revisiting Intermediate-Layer Matching in Knowledge Distillation: Layer-Selection Strategy Doesn't Matter (Much)

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