Encoder Decoder Transformer

Encoder-decoder transformers are neural network architectures designed for sequence-to-sequence tasks, aiming to map input sequences (e.g., images, text, time series) to output sequences. Current research focuses on improving efficiency and robustness, particularly through novel attention mechanisms (e.g., channel modulation self-attention) and architectural modifications like incorporating local features or handling unbounded input lengths. These models are proving highly effective across diverse applications, including image deblurring, machine translation, and time series prediction, demonstrating their versatility and potential to advance various scientific fields and practical technologies.