Auto Decoder

Auto-decoders are neural networks designed to reconstruct input data from a compressed representation, finding applications across diverse scientific and engineering domains. Current research focuses on improving their accuracy and efficiency for specific tasks, employing architectures like implicit neural representations (SIRENs), multi-layer perceptrons (MLPs) with novel spatial operations, and transformer-based models. These advancements are driving progress in areas such as robotics control, time series imputation, image segmentation, and solving partial differential equations, offering improved accuracy and reduced computational demands compared to traditional methods.