encodercentric
encodercentric refers to a design philosophy or approach in artificial intelligence and machine learning where the encoder component of a model is considered the primary or most crucial element. This often applies to architectures like autoencoders, sequence-to-sequence models, or transformers, where the encoder's task is to process input data and compress it into a meaningful, dense representation.
In such systems, the encoder's effectiveness directly impacts the quality of the latent space or embedding
This approach contrasts with architectures where the decoder or another part of the model might be given