Based Loss
Based loss functions optimize models by directly focusing on the ranking of predictions, rather than solely on individual prediction scores, aligning training with evaluation metrics like average precision. Current research emphasizes improving the efficiency of these losses, particularly for large datasets, through techniques like bucketing and developing differentiable surrogates for non-differentiable ranking operators. This approach shows promise across diverse applications, including object detection, edge detection, image retrieval, and text ranking, leading to improved performance and addressing challenges like class imbalance and label uncertainty.
Papers
October 11, 2024
July 19, 2024
March 4, 2024
September 15, 2023
October 12, 2022
August 5, 2022
July 18, 2022
June 6, 2022
April 4, 2022