Encoder DEcoder

Encoder-decoder models are a fundamental architecture in machine learning, aiming to learn mappings between input and output data representations. Current research focuses on improving these models through pre-training on large datasets, often employing transformer-based architectures and incorporating techniques like masked modeling and generative adversarial training to enhance performance on diverse downstream tasks, including image recognition, natural language processing, and graph-to-text generation. This work is significant because it enables the development of more robust and versatile models applicable across various domains, leading to advancements in areas like computer vision, natural language understanding, and code generation.