Pre Trained Decoder

Pre-trained decoders are revolutionizing various machine learning tasks by leveraging pre-existing knowledge to improve efficiency and performance in downstream applications. Current research focuses on adapting pre-trained decoders for specific tasks, such as scene text recognition, machine translation, and time series forecasting, often employing techniques like prompt tuning or modality adaptation to bridge the gap between pre-training and fine-tuning. This approach is particularly valuable in scenarios with limited labeled data, enabling significant improvements in model accuracy and reducing computational costs compared to training from scratch. The resulting advancements have broad implications across diverse fields, including natural language processing, computer vision, and speech recognition.