Hyper Transformer

Hyper-Transformers are a class of neural networks using transformer architectures to generate parameters for other neural networks, often called hypernetworks. Current research focuses on improving efficiency and accuracy in various applications, including video encoding/decoding, image generation and completion, and multi-modal data processing, employing techniques like attention mechanisms and dynamic convolutions within the hypernetwork design. This approach offers potential for faster and more efficient training and inference in diverse machine learning tasks, leading to advancements in areas such as computer vision and natural language processing.