The field of neural networks is moving towards more efficient and adaptable architectures. Recent developments focus on reducing model size and complexity while maintaining accuracy, which is crucial for large-scale deployments and resource-constrained environments. Innovations in pruning mechanisms, incremental learning, and neural architecture search are leading to more scalable and efficient solutions. Notably, advancements in training algorithms and optimization techniques are enabling the generation of compressed models with minimal accuracy degradation. Noteworthy papers include:

• A work on a training algorithm that extends the capabilities of Neural Metamorphosis to enable full-network metamorphosis, allowing for scalable and efficient deployment of deep models.
• A study on a hybrid method combining Principal Component Analysis with a deep neural network optimized by the Grasshopper Optimization Algorithm, achieving remarkable fault detection accuracy in Wireless Sensor Networks.
• A novel pruning mechanism that reduces the size of neural networks while preserving accuracy, providing a less computationally intensive alternative to current methods.
• A framework that adapts model structure dynamically for incremental learning, reducing forgetting and enhancing accuracy while maintaining a lower model size.