UHKD: A Unified Framework for Heterogeneous Knowledge Distillation via Frequency-Domain Representations

• URL: http://arxiv.org/abs/2510.24116v1
• Date: Tue, 28 Oct 2025 06:41:43 GMT
• Title: UHKD: A Unified Framework for Heterogeneous Knowledge Distillation via Frequency-Domain Representations
• Authors: Fengming Yu, Haiwei Pan, Kejia Zhang, Jian Guan, Haiying Jiang,
• Abstract summary: Unified Heterogeneous Knowledge Distillation (UHKD) is proposed as a framework that leverages intermediate features in the frequency domain for cross-architecture transfer.<n>Experiments on CIFAR-100 and ImageNet-1K demonstrate gains of 5.59% and 0.83% over the latest method.
• Score: 5.382357091398666
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Knowledge distillation (KD) is an effective model compression technique that transfers knowledge from a high-performance teacher to a lightweight student, reducing cost while maintaining accuracy. In visual applications, where large-scale image models are widely used, KD enables efficient deployment. However, architectural diversity introduces semantic discrepancies that hinder the use of intermediate representations. Most existing KD methods are designed for homogeneous models and degrade in heterogeneous scenarios, especially when intermediate features are involved. Prior studies mainly focus on the logits space, making limited use of the semantic information in intermediate layers. To address this limitation, Unified Heterogeneous Knowledge Distillation (UHKD) is proposed as a framework that leverages intermediate features in the frequency domain for cross-architecture transfer. Fourier transform is applied to capture global feature information, alleviating representational discrepancies between heterogeneous teacher-student pairs. A Feature Transformation Module (FTM) produces compact frequency-domain representations of teacher features, while a learnable Feature Alignment Module (FAM) projects student features and aligns them via multi-level matching. Training is guided by a joint objective combining mean squared error on intermediate features with Kullback-Leibler divergence on logits. Experiments on CIFAR-100 and ImageNet-1K demonstrate gains of 5.59% and 0.83% over the latest method, highlighting UHKD as an effective approach for unifying heterogeneous representations and enabling efficient utilization of visual knowledge

Related papers

• Heterogeneous Complementary Distillation [16.315256873831064]
  Heterogeneous Complementary Distillation (HCD) integrates complementary teacher and student features to align representations in shared logits.<n> experiments on the CIFAR-100, Fine-grained (e.g., CUB200) and ImageNet-1K datasets demonstrate that HCD outperforms state-of-the-art KD methods.
  arXiv  Detail & Related papers  (2025-11-14T04:06:33Z)
• Manifold-aware Representation Learning for Degradation-agnostic Image Restoration [135.90908995927194]
  Image Restoration (IR) aims to recover high quality images from degraded inputs affected by various corruptions such as noise, blur, haze, rain, and low light conditions.<n>We present MIRAGE, a unified framework for all in one IR that explicitly decomposes the input feature space into three semantically aligned parallel branches.<n>This modular decomposition significantly improves generalization and efficiency across diverse degradations.
  arXiv  Detail & Related papers  (2025-05-24T12:52:10Z)
• FiGKD: Fine-Grained Knowledge Distillation via High-Frequency Detail Transfer [0.0]
  Fine-Grained Knowledge Distillation (FiGKD) is a frequency-aware framework that decomposes a model's logits into low-frequency (content) and high-frequency (detail) components.<n>FiGKD consistently outperforms state-of-the-art logit-based and feature-based distillation methods across a variety of teacher-student configurations.
  arXiv  Detail & Related papers  (2025-05-17T08:27:02Z)
• Contrastive Representation Distillation via Multi-Scale Feature Decoupling [2.0401874889991887]
  This work proposes MSDCRD, a model-agnostic distillation framework that systematically decouples global features into multi-scale local features.<n>Extensive experiments demonstrate that MSDCRD achieves superior performance not only in homogeneous teacher-student settings but also in heterogeneous architectures.
  arXiv  Detail & Related papers  (2025-02-09T10:03:18Z)
• Fuse Before Transfer: Knowledge Fusion for Heterogeneous Distillation [52.0297393822012]
  We introduce an assistant model as a bridge to facilitate smooth feature knowledge transfer between heterogeneous teachers and students.<n>Within our proposed design principle, the assistant model combines the advantages of cross-architecture inductive biases and module functions.<n>Our proposed method is evaluated across some homogeneous model pairs and arbitrary heterogeneous combinations of CNNs, ViTs, spatial KDs.
  arXiv  Detail & Related papers  (2024-10-16T08:02:49Z)
• An Information Compensation Framework for Zero-Shot Skeleton-based Action Recognition [49.45660055499103]
  Zero-shot human skeleton-based action recognition aims to construct a model that can recognize actions outside the categories seen during training. Previous research has focused on aligning sequences' visual and semantic spatial distributions. We introduce a new loss function sampling method to obtain a tight and robust representation.
  arXiv  Detail & Related papers  (2024-06-02T06:53:01Z)
• Aligning in a Compact Space: Contrastive Knowledge Distillation between Heterogeneous Architectures [4.119589507611071]
  We propose a Low-Frequency Components-based Contrastive Knowledge Distillation (LFCC) framework that significantly enhances the performance of feature-based distillation. Specifically, we designe a set of multi-scale low-pass filters to extract the low-frequency components of intermediate features from both the teacher and student models. We show that LFCC achieves superior performance on the challenging benchmarks of ImageNet-1K and CIFAR-100.
  arXiv  Detail & Related papers  (2024-05-28T18:44:42Z)
• Attention-guided Feature Distillation for Semantic Segmentation [8.344263189293578]
  This paper showcases the efficacy of a simple yet powerful method for utilizing refined feature maps to transfer attention.<n>The proposed Attention-guided Feature Distillation (AttnFD) method, employs the Convolutional Block Attention Module (CBAM)<n>It achieves state-of-the-art results in terms of improving the mean Intersection over Union (mIoU) of the student network on the PascalVoc 2012, Cityscapes, COCO, and CamVid datasets.
  arXiv  Detail & Related papers  (2024-03-08T16:57:47Z)
• One-for-All: Bridge the Gap Between Heterogeneous Architectures in Knowledge Distillation [69.65734716679925]
  Knowledge distillation has proven to be a highly effective approach for enhancing model performance through a teacher-student training scheme. Most existing distillation methods are designed under the assumption that the teacher and student models belong to the same model family. We propose a simple yet effective one-for-all KD framework called OFA-KD, which significantly improves the distillation performance between heterogeneous architectures.
  arXiv  Detail & Related papers  (2023-10-30T11:13:02Z)
• Knowledge Diffusion for Distillation [53.908314960324915]
  The representation gap between teacher and student is an emerging topic in knowledge distillation (KD) We state that the essence of these methods is to discard the noisy information and distill the valuable information in the feature. We propose a novel KD method dubbed DiffKD, to explicitly denoise and match features using diffusion models.
  arXiv  Detail & Related papers  (2023-05-25T04:49:34Z)
• DeepEMD: Differentiable Earth Mover's Distance for Few-Shot Learning [122.51237307910878]
  We develop methods for few-shot image classification from a new perspective of optimal matching between image regions. We employ the Earth Mover's Distance (EMD) as a metric to compute a structural distance between dense image representations. To generate the important weights of elements in the formulation, we design a cross-reference mechanism.
  arXiv  Detail & Related papers  (2020-03-15T08:13:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.