The field of human activity recognition and image classification is moving towards developing more robust and domain-invariant models. Researchers are exploring new architectures and techniques to improve the generalization capability of models across different domains and datasets. One of the key challenges in human activity recognition is addressing cross-user variability, and recent studies have proposed innovative solutions such as integrating anatomical correlation knowledge into graph neural networks and using variational edge feature extractors. In image classification, researchers are focusing on developing domain-invariant features through neuro-inspired neural response normalization layers and higher-order convolutional neural networks. Notably, the TAROT algorithm has shown superior performance in robust domain adaptation, and the NeuRN layer has demonstrated effectiveness in enhancing domain generalization. The paper on Domain-Adversarial Anatomical Graph Networks is particularly noteworthy for its state-of-the-art performance on the OPPORTUNITY and DSADS datasets. The paper on TAROT is also notable for its theoretical justification and superior performance on the DomainNet dataset.