Single Decoder

Single decoder models are emerging as a powerful approach across diverse machine learning tasks, aiming to improve efficiency and flexibility by unifying multiple sub-tasks within a single decoding component. Current research focuses on developing architectures like decomposed or compositional decoders, often within the context of variational autoencoders or transformers, to handle complex data distributions and multiple output modalities (e.g., image pixels and semantic labels, multiple speech speakers, or various musical instruments). This approach offers advantages in parameter efficiency, improved generalization across tasks, and enhanced interpretability of latent spaces, impacting fields ranging from medical image analysis and federated learning to speech transcription and music source separation.