Haipeng Wang

Cuipeng Wang, Haipeng Wang

Knowledge distillation enhances the performance of compact student networks by transferring knowledge from more powerful teacher networks without introducing additional parameters. In the feature space, local regions within an individual global feature encode distinct yet interdependent semantic information. Previous feature-based distillation methods mainly emphasize global feature alignment while neglecting the decoupling of local regions within an individual global feature, which often results in semantic confusion and suboptimal performance. Moreover, conventional contrastive representation distillation suffers from low efficiency due to its reliance on a large memory buffer to store feature samples. To address these limitations, this work proposes MSDCRD, a model-agnostic distillation framework that systematically decouples global features into multi-scale local features and leverages the resulting semantically rich feature samples with tailored sample-wise and feature-wise contrastive losses. This design enables efficient distillation using only a single batch, eliminating the dependence on external memory. Extensive experiments demonstrate that MSDCRD achieves superior performance not only in homogeneous teacher-student settings but also in heterogeneous architectures where feature discrepancies are more pronounced, highlighting its strong generalization capability.

Xiayang Xiao, Zhuoxuan Li, Ruyi Zhang et al.

The limitations of existing Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) methods lie in their confinement by the closed-environment assumption, hindering their effective and robust handling of unknown target categories in open environments. Open Set Recognition (OSR), a pivotal facet for algorithmic practicality, intends to categorize known classes while denoting unknown ones as "unknown." The chief challenge in OSR involves concurrently mitigating risks associated with generalizing features from a restricted set of known classes to numerous unknown samples and the open space exposure to potential unknown data. To enhance open-set SAR classification, a method called scattering kernel with reciprocal learning network is proposed. Initially, a feature learning framework is constructed based on reciprocal point learning (RPL), establishing a bounded space for potential unknown classes. This approach indirectly introduces unknown information into a learner confined to known classes, thereby acquiring more concise and discriminative representations. Subsequently, considering the variability in the imaging of targets at different angles and the discreteness of components in SAR images, a proposal is made to design convolutional kernels based on large-sized attribute scattering center models. This enhances the ability to extract intrinsic non-linear features and specific scattering characteristics in SAR images, thereby improving the discriminative features of the model and mitigating the impact of imaging variations on classification performance. Experiments on the MSTAR datasets substantiate the superior performance of the proposed approach called ASC-RPL over mainstream methods.