Explanation-guided learning (EGL) has shown promise in aligning model predictions with interpretable reasoning, particularly in computer vision tasks. However, most approaches rely on external annotations or heuristic-based segmentation to supervise model explanations, which can be noisy, imprecise and difficult to scale. In this work, we provide both empirical and theoretical evidence that low-quality supervision signals can degrade model performance rather than improve it. In response, we propose ALIGN, a novel framework that jointly trains a classifier and a masker in an iterative manner. The masker learns to produce soft, task-relevant masks that highlight informative regions, while the classifier is optimized for both prediction accuracy and alignment between its saliency maps and the learned masks. By leveraging high-quality masks as guidance, ALIGN improves both interpretability and generalizability, showing its superiority across various settings. Experiments on the two domain generalization benchmarks, VLCS and Terra Incognita, show that ALIGN consistently outperforms six strong baselines in both in-distribution and out-of-distribution settings. Besides, ALIGN also yields superior explanation quality concerning sufficiency and comprehensiveness, highlighting its effectiveness in producing accurate and interpretable models.

Deep neural networks (DNNs) have achieved remarkable performance in many tasks, yet they often behave as opaque black boxes. Explanation-guided learning (EGL) methods steer DNNs using human-provided explanations or supervision on model attributions. These approaches improve interpretability but typically assume benign inputs and incur heavy annotation costs. In contrast, both predictions and saliency maps of DNNs could dramatically alter facing imperceptible perturbations or unseen patterns. Adversarial training (AT) can substantially improve robustness, but it does not guarantee that model decisions rely on semantically meaningful features. In response, we propose Explanation-Guided Adversarial Training (EGAT), a unified framework that integrates the strength of AT and EGL to simultaneously improve prediction performance, robustness, and explanation quality. EGAT generates adversarial examples on the fly while imposing explanation-based constraints on the model. By jointly optimizing classification performance, adversarial robustness, and attributional stability, EGAT is not only more resistant to unexpected cases, including adversarial attacks and out-of-distribution (OOD) scenarios, but also offer human-interpretable justifications for the decisions. We further formalize EGAT within the Probably Approximately Correct learning framework, demonstrating theoretically that it yields more stable predictions under unexpected situations compared to standard AT. Empirical evaluations on OOD benchmark datasets show that EGAT consistently outperforms competitive baselines in both clean accuracy and adversarial accuracy (+37%) while producing more semantically meaningful explanations, and requiring only a limited increase (+16%) in training time.

A palmprint recognition approach based on multi‐wavelet and convolutional neural network (CNN) against noise and misalignment is given. CNN method has high robustness in biometrics, but a large number of training samples are necessary. Moreover, the gathered palmprint images should be cropped to obtain their region of interest (ROI) and noise pollution and misalignment are not well solved. Therefore, the original training database is augmented to reduce the effects of noise and misalignment. First, an original training palmprint image is split into five new images, and every new image is decomposed once by multi‐wavelet. Three lower frequency bands in the low‐frequency multi‐wavelet component corresponding to pre‐filters are extracted as three samples. Furthermore, the split image is downsampled as a new sample. Second, the CNN model is constructed based on the augmented database by experiments. Third, the softmax method is used to classify the test samples. At last, the final result is obtained from 20 results by using the voting method. The experimental results based on PolyU, CASIA, and IIT Delhi Touchless Palmprint Database palmprint databases show that the proposed method can effectively recognise palmprint with high robustness while there is noise and misalignment, and has a generalisation to other palmprint databases.