Best Effort Adaptation

Best-effort adaptation focuses on improving model performance on a target domain with limited labeled data by leveraging knowledge from a source domain with abundant data. Current research explores various techniques, including sample reweighting, multi-level encoders, and contrastive learning, often within specific architectures like autoregressive transducers or for tasks such as optical flow estimation and semantic segmentation. This approach is significant because it addresses the common challenge of data scarcity in many applications, leading to more robust and adaptable models across diverse domains.