PILO: Principal Component-based Implicit Regularization with Low-rank Optimization for Robust Transfer Learning

Joint Adversarial and Subdomain Adaptation: A Dual-Strategy Framework for Robust Cross-Domain Modulation Recognition

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.

Adversarial Purification by Consistency-aware Latent Space Optimization on Data Manifolds.

This paper reveals that samples generated by a well-trained generative model are close to clean ones but far from adversarial ones, and proposes Consistency Model-based Adversarial Purification (CMAP), which optimizes vectors within the latent space of a pre-trained consistency model to generate samples for restoring clean data.

Adversarial Training Without Input Gradients via Low-Rank Householder Expansions

This work concerns adversarial training against the small-norm adversarial examples that arise from the inherent input instability of a trained deep neural network. Examples in this class are small as measured in the relative $\ell^2$-norm, and therefore lie in the neighborhood of the input on which the model acts approximately linearly, the regime in which the perturbation remains imperceptible. We first show that such examples can be computed directly from the trained network parameters, without input gradient iterations, by means of a linearization called the low-rank Householder expansion (LRHE). The expansion describes the composed affine map rather than any individual layer, and the directions it identifies are read from the activation pattern already available in the forward pass. We then propose a simple adversarial training scheme built on this construction. No differentiation with respect to the input is performed at any point: training requires only additional forward evaluations, with weight parameters updated by the standard backward pass, and the inner maximization of the usual min-max formulation is eliminated entirely. That such a regularizer exists is our main finding: the methods that dispense with the inner search all obtain their local geometry by differentiating with respect to the input, and we show this is not necessary. The regularizer costs the equivalent of $2.8$ PGD steps per epoch, an $8.7\times$ reduction relative to 40-step adversarial training on MNIST and below the cost of 3-step training. The resulting models match three-step PGD adversarial training for relative $\ell^2$ budgets $\varepsilon \le 0.02$ and 40-step training for $\varepsilon \le 0.012$, falling away beyond, consistent with the locality of the expansion.

Progressive Adversarial Multi-View Alignment for Unsupervised Embedded Feature Selection with Linear Complexity

Diverge to Converge: Mutual Heterogeneous Learning for Robust Pruning

Mutual Heterogeneous Learning (MHL) is proposed, a framework enabling robust pruning via single-model inference that significantly outperforms single-model baselines in both adversarial robustness and corruption robustness, while maintaining competitive clean accuracy.

MC-SNN: Multicenter Stochastic Neural Network for Adversarially Robust Learning.

A multicenter learning method that leverages the advantage of stochastic neural networks (SNNs) for feature uncertainty learning and induces multiple centers for each class of samples in latent space to fit data more delicately, named the multicenter SNN (MC-SNN).