Single-Teacher View Augmentation: Enhancing Knowledge Distillation with Student-Guided Perturbations

2026-07-23 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, extended

Summary

Shift-Augmented Knowledge Distillation (SAKD) is a novel framework that enhances knowledge distillation by generating diverse teacher views using student-guided perturbations. Unlike multi-teacher or two-stage methods like Angular-KD, SAKD employs a single-stage training process, utilizing the student's evolving features to dynamically create adaptive perturbations. These perturbations are then expanded into multiple distinct views via a parameter-free cyclic shift operation. Experiments on CIFAR-100 and ImageNet demonstrate SAKD's efficiency and accuracy. It achieves 75.54% on CIFAR-100 (R32x4→R8x4) and 71.12% top-1 on ImageNet (ResNet-34→ResNet-18), outperforming random perturbation methods like TeKAP and matching two-stage approaches while reducing extra parameters by 83% (46K vs 276K for N=3) and eliminating pre-training. The optimal configuration uses α=0.9 and N=3 views.

Key takeaway

For MLOps engineers or AI scientists deploying models to resource-constrained edge devices, SAKD offers a compelling knowledge distillation solution. Its single-stage training and constant parameter complexity significantly reduce computational overhead and development time compared to multi-stage methods. You can achieve competitive accuracy with 83% fewer parameters and no pre-training, making it ideal for efficient model compression without sacrificing performance.

Key insights

Student-guided perturbations and cyclic shifts enable efficient, diverse knowledge distillation in a single training stage.

Principles

• Perturbations should complement, not reconstruct, teacher knowledge.
• Student features offer dynamic conditioning for view generation.
• Cyclic shifts provide structured, parameter-free view diversity.

Method

A lightweight MLP, conditioned on student features, generates a base perturbation π₀. This π₀ is expanded into N views via cyclic shifts π₁=\text{CyclicShift}(π₀,δ₁). These views are mixed with teacher logits •ⁿ using •₁ⁿ=α•ⁿ+(1-α)π₁.

In practice

• Use α=0.9 to balance teacher dominance and perturbation diversity.
• Consider N=3 views for optimal diversity with cyclic shifts.

Topics

• Knowledge Distillation
• Model Compression
• Teacher Augmentation
• Student-Guided Perturbations
• Cyclic Shift
• Image Classification

Best for: Research Scientist, AI Engineer, Computer Vision Engineer, AI Scientist, Machine Learning Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.