Aug 2026· International Conference on Optoelectronic Information and Computer Engineering (OICE)· Vol 14317, pp. 1431715 - 1431715-7· 0 citations· 14 references
Engineering
TL;DR
Experimental results show that TransPileSiam consistently improves downstream recognition under limited-label conditions and improves robustness, label efficiency, and cross-domain generalization for practical SEI tasks.
Abstract
Specific emitter identification (SEI) is challenged by both limited labeled data and domain shifts caused by variations in acquisition conditions, devices, and propagation environments. To address these issues, this paper proposes TransPileSiam, a domain-aware self-supervised pretraining framework for few-shot SEI. Built upon SimSiam, the proposed method constructs multiple augmented views from each raw IQ sample and performs multi-view consistency learning to improve representation robustness under complex perturbations. To further enhance cross-domain transferability, a gradient-reversal-based domain-adversarial regularization is introduced to suppress domain-specific information in the learned features. The pretrained encoder is then adapted to downstream SEI tasks through few-shot fine-tuning. Experimental results show that TransPileSiam consistently improves downstream recognition under limited-label conditions. In particular, it improves the test accuracy of a ResNet34-based supervised model by 3.32% and yields an average gain of about 1% in few-shot evaluation. These results demonstrate that TransPileSiam effectively improves robustness, label efficiency, and cross-domain generalization for practical SEI tasks.
While deep learning has significantly advanced automatic modulation recognition in complex environments, its performance is often limited by domain shifts caused by factors like channel fading and frequency offset. Domain adaptation has emerged as the primary paradigm to address this challenge. However, existing methods face a critical trade-off, as global alignment strategies tend to disrupt class-specific structures, while local alignment methods are overly sensitive to the quality of pseudo-labels. To address this trade-off, this paper proposes a joint adversarial and subdomain adaptation network (JASA-Net) centered on a dual domain adaptation (DDA) strategy. This strategy employs a “global-first, then-local” alignment ap-proach, where an initial global adversarial alignment establishes a strong foundation for generating high-quality pseudo-labels that subsequently guide a local alignment via the local maximum mean discrepancy (LMMD) metric. To extract robust features, we design a patch adaptive multimodal transformer (PAMT) encoder. Furthermore, an efficient “source domain warm-up + aggressive scheduling” training strategy is developed to enhance performance. Extensive experiments on custom-simulated datasets demonstrate that the proposed framework significantly outperforms representative baselines across various cross-domain tasks. Notably, it exhibits remarkable robustness in few-shot scenarios. This work also provides a critical analysis of the task-dependent nature of entropy weighting, offering valuable insights for future research in the field.
Ming Cheng, Wen Deng, Jintian Xiong et al.· IEEE Transactions on Cogniti...· 0 citations
This work proposes MeanFlow-Transfer, which maps heterogeneous source outputs into a shared velocity representation, uses it to initialize an MF generator from the source weights, and optimizes an MF objective on the target domain, and introduces Continuous Adversarial MeanFlow, a post-training stage that extends continuous adversarial flow models from instantaneous velocities to MF's finite-interval average velocities.
Yara Bahram, Zahra Dehghani, M. Desbos et al.· 0 citations
Generalizable vehicle re-identification (ReID) seeks to develop models capable of adapting to previously unseen domains without additional fine-tuning or retraining. Most existing approaches attempt to learn domain-invariant representations by aligning data distributions across source domains. However, they often neglect the inherent domain-related redundancy within source images, which suppresses the learning of complementary features characterized by lower occurrence probabilities and weaker activations. To overcome this limitation, we introduce Unity in Diversity (UID), a framework of multi-expert knowledge adversarial learning and collaboration. UID incorporates a training-free mechanism to filter out domain-related redundancy in source images, thereby promoting the learning of complementary feature representations. Specifically, we design a Spectrum-based Transformation for Redundancy Elimination and Augmentation Module (STREAM), which generates two distinct types of image inputs for a two-stage complementary feature learning process. In the multi-expert knowledge adversarial learning phase, STREAM enables the model to acquire a diversified identity-oriented prompt set that captures subtle but discriminative visual cues critical for distinguishing highly similar vehicles. This multi-expert prompt set is progressively integrated into complementary feature representations through the proposed knowledge confrontation and collaboration mechanism, which substantially enhances the model's ability to extract fine-grained and complementary information. Extensive experiments conducted on multiple benchmarks demonstrate that UID achieves state-of-the-art performance, validating its effectiveness and generalizability. Our code is available at https://github.com/KZYYYY/UID.
Zhenyu Kuang, Hongyang Zhang, Xiaosong Li et al.· IEEE Transactions on Image P...· 0 citations
Deep learning has boosted remote sensing (RS) scene classification, but adversarial examples can still cause high-confidence misclassification with imperceptible perturbations. Adversarial purification (AP) offers a practical test-time defense without retraining the classifier. However, most existing methods are confined to pixel-space restoration, which may leave residual adversarial effects that persist and amplify through feature extraction, ultimately biasing the prediction. To address these issues, a dual-domain AP (DDAP) framework is proposed to mitigate adversarial effects at both the pixel and feature levels in a unified pipeline. In the pixel domain, a pixel-domain frequency-aware diffusion purification (PFDP) module performs diffusion-based restoration through a frequency-aware dual-stream U-Net (FD-UNet). By integrating adaptive spectral filtering with multidomain consistency constraints, PFDP reduces adversarial-perturbation-dominated high-frequency responses while preserving structural details and semantic information in RS imagery. In the feature domain, an adversarial vulnerable channel dropout (AVCD) strategy models unshifted shallow-feature statistics with a Gaussian mixture model (GMM) and adaptively assigns channelwise dropout probabilities based on a samplewise shift score and channel vulnerability, thereby suppressing residual adversarial influence before downstream classification. Extensive experiments on UC Merced (UCM) and aerial image dataset (AID) across multiple backbones and attack types demonstrate that DDAP consistently improves robustness while maintaining a favorable clean–robust balance compared with representative baselines.
Yuru Su, Shaohui Mei, Mingyang Ma et al.· IEEE Transactions on Geoscie...· 0 citations
3D unsupervised domain adaptive (UDA) segmentation mitigates the high cost of manual annotations of the new domain data. Self-training has emerged as the dominant approach in this area, where its success heavily depends on a well-initialized warm-up model to generate reliable pseudo labels. However, existing methods often depend on source supervision or output-level adversarial alignment to obtain the warm-up model, which suffer from limited generalization and training instability due to the large domain gap between domains. Constructing domain-similar representations is an effective way to bridge this gap. In this work, we propose CVKD-UDA, which revisits voxel size as a core design factor to construct domain-similar representations and leverages cross-view complementary cues to balance transferability and discriminability of the warm-up model. First, we generate two complementary views by varying voxel sizes and introduce a cross-view knowledge distillation (CVKD) to enhance generalization and target perception of the model. Second, to balance transferability and discriminability, we design a lightweight Decouple-Adapter and an auxiliary imitation classifier to decouple cross-view knowledge transfer. Extensive experiments on two benchmarks demonstrate that CVKD-UDA effectively improves the performance of self-training methods and provides a new perspective for 3D UDA segmentation. Our code will be available at GitHub.
Zhimin Yuan, Ming Cheng, Shangshu Yu et al.· IEEE transactions on multime...· 0 citations
MAIG-Net combines a target-label-free ground-sampling-distance rule, an intermediate domain constructed by Fourier domain adaptation (FDA) that transfers only low-frequency target appearance onto labeled source images while preserving the complete source phase and road labels, and Domain-Invariant Feature Alignment modules that perform reliability-weighted, topology-conditioned adversarial alignment at three encoder depths.
Chengqi Bao, Guangwu Chen, Wenbo Jin et al.· Italian National Conference...· 0 citations
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