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

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