Zero Padding

Zero padding, the practice of adding extra data points (often zeros) to datasets or model inputs, is a widely used technique in machine learning and signal processing aimed at improving efficiency and performance. Current research focuses on optimizing padding strategies within various architectures, including convolutional neural networks (CNNs), generative adversarial networks (GANs), and large language models (LLMs), exploring alternatives to traditional zero padding to mitigate issues like information loss during aggregation or the encoding of spurious positional information. These advancements are significant because they directly impact the efficiency, accuracy, and robustness of numerous machine learning applications, from natural language processing and image generation to federated learning and speaker recognition.