Encoder Side

Encoder-side research focuses on developing robust and efficient methods for extracting meaningful representations from diverse input data, such as speech, images, and code. Current efforts concentrate on improving encoder performance through multi-task learning, multimodal fusion, and efficient architectures like transformers and U-Nets, often incorporating techniques like knowledge distillation and contrastive learning. These advancements are crucial for improving the accuracy and efficiency of various downstream tasks, ranging from speech recognition and code generation to drug discovery and medical image analysis. The resulting improvements in feature extraction have significant implications for numerous fields, enabling more accurate and efficient applications across diverse domains.