Aug 2026· IEEE Transactions on Image Processing· Vol 35, pp. 9154-9168· 0 citations· 81 references
Medicine
Abstract
Knowledge distillation (KD) has become a pivotal technique for transferring knowledge from large-scale teacher models to lightweight student models. However, traditional feature-based distillation methods necessitate the direct exposure of the teacher’s intermediate representations, raising concerns regarding data privacy and the leakage of proprietary model details. These concerns often hinder the deployment of distillation in collaborative or cloud-based scenarios. To address these challenges, we propose a privacy-aware teacher-oriented projected feature distillation framework that reduces direct feature exposure while preserving distillation effectiveness. Specifically, our method employs a low-rank projection strategy to obfuscate the teacher’s features into a compact subspace. Authorized student models leverage this projection matrix to align their own features, enabling effective knowledge transfer while reducing direct exposure of the teacher’s original spatial patterns. Extensive experiments on benchmark datasets demonstrate empirical feature obfuscation and reconstruction resistance while maintaining competitive performance in object detection and semantic segmentation, with gains in several settings even when learning from projected representations.
Deploying deep learning models on resource-constrained edge devices necessitates a trade-off between computational efficiency and robustness. While Knowledge Distillation (KD) is widely used to compress large models into lightweight students, the impact of the Teacher’s architectural inductive bias on the Student’s safety profile remains underexplored. This paper investigates Cross-Architecture Distillation, specifically examining whether the robustness properties of a Vision Transformer (ViT) Teacher can be transferred to a Convolutional Neural Network (CNN) Student (MobileNetV2). Using Centered Kernel Alignment (CKA) and Fourier Spectral Analysis, we demonstrate that robustness transfer is driven by the learning of global shape biases— preserving the teacher's functional geometry—rather than strict feature mimicry. To isolate architectural bias from model capacity confounds and validate scalability to high-resolution data, our experiments are conducted on ImageNet-1k. Results reveal that a MobileNetV2 distilled from a ViT-B/16 Teacher achieves a significantly lower mean corruption error (mCE) compared to one distilled from a massive, capacity-matched ResNet-152 (p < 0.0001, Cohen's d = 21.50), despite comparable clean accuracy. Furthermore, we show that the ViT-distilled student effectively suppresses high-frequency noise, validating its resilience against adversarial perturbations. These findings establish a pareto-optimal strategy for deploying robust, lightweight models, proving that the choice of Teacher architecture is a critical hyperparameter for safety-critical edge applications.
Tamuno Opubo Dappa, Somtochukwu Anunobi· International Conference on...· 0 citations
Quantization-Aware Training (QAT) enables the deployment of quantized models with minimal accuracy degradation. However, in practical scenarios, training labels are often unavailable due to privacy, copyright, or cost constraints. Knowledge Distillation (KD) is a common approach to address this challenge, but we observe that prior work combining QAT with KD suffers from a fundamental limitation: during distillation, the range mismatch between the teacher and the quantized student model induces an unattainable residual, resulting in an irreducible lower bound on the distillation loss. Motivated by this observation, we propose SQuaT (Student-Aware Quantized Teacher Features), a label-free QAT framework with KD that theoretically eliminates this lower bound by applying the student's quantization parameters to quantize the teacher's features during distillation. Through comprehensive experiments across diverse settings, we demonstrate that SQuaT consistently outperforms strong baselines, with particularly pronounced gains in extreme low-bit (e.g., 1- and 2-bit) settings. Furthermore, extensive evaluations across various model design choices show that our approach does not rely on specific architectural assumptions, making it broadly applicable across diverse architectures and quantization settings. The source code is available at https://github.com/lcdbsa522/SQuaT.
H. Lee, Hyeonsik Jo, Jinwook Chung et al.· 0 citations
Shift-Augmented Knowledge Distillation (SAKD) is proposed, a simple yet effective framework that leverages the student's evolving features as a dynamic condition for perturbation generation, enabling single-stage training while producing adaptive, diverse views through a parameter-free cyclic shift.
Xuyi Yu, Yaohua Liu, Chengjun Li et al.· 0 citations
This work revisits on-policy Reverse Kullback-Leibler distillation and decomposes its objective into a teacher-fitting term and a student-entropy term, without introducing an explicit FKL branch, and proposes Adaptive Entropy Distillation (AED), which uses the teacher's entropy to dynamically calibrate token-level imitation strength.
Shizheng Li, Zhiyu Shen, Yuyin Lu et al.· 0 citations
GeoSD, a geometric self-distillation objective that treats this drift as movement in the student's predictive behavior and counters it in two complementary ways, preserves the in-distribution gains of self-distillation while improving average OOD accuracy.