Encoder Decoder Model

Encoder-decoder models are a class of neural networks designed for sequence-to-sequence tasks, aiming to map an input sequence (e.g., an image, audio, or text) to an output sequence (e.g., a caption, translation, or code). Current research emphasizes improving efficiency and robustness, exploring architectures like Transformers and LSTMs, and incorporating techniques such as contrastive learning, adversarial training, and direct preference optimization to enhance performance across diverse applications. These models are proving highly impactful, enabling advancements in various fields including machine translation, speech recognition, image captioning, and even biological sequence analysis.