# Finetuning with Encouraging Loss (EL) Below we provide methods for finetuning with label smoothed encouraging loss proposed in [_Well-classified Examples are Underestimated in Classification with Deep Neural Networks_](https://arxiv.org/pdf/2110.06537.pdf) on different downstream tasks. The implementation is in [label_smoothed_encouraging_loss.py](criterions/label_smoothed_encouraging_loss.py). You can set the `--criterion` to `adjust_label_smoothed_encouraging_loss` to use it. This criterion has a hyper-parameter `--log-end`. `--log-end < 1` results in a approximated and conservative version of the full encouraging loss. A high log_end will more strongly weaken the gradient vanishing, enhance the modeling of the data, and increase the growth rate of the margin, but it will also bring a larger gradient norm, which will bring challenges to the existing optimization system. We recommend higher log_end for cases with higher performance, and 0.75 or 0.5 as your first try. ## Image Captioning We provide procedures for image captioning with EL below. The preprocessing is identical to default setting. <details> <summary><b>Finetuning</b></summary> <p> We propose two scripts for stage1. </b> </p> <pre> cd run_scripts/caption nohup sh train_caption_stage1_el.sh > train_stage1_el.out & # stage 1, train with encouraging loss, expected cider 1.403 nohup sh train_caption_stage1_el_db.sh > train_stage1_el.out & # stage 1, train with encouraging loss, and drop best examples, expected cider 1.404 </pre> </details> ## Referring Expression Comprehension We provide procedures for image captioning with EL below. The preprocessing is identical to default setting. <details> <summary><b>Finetuning</b></summary> <pre> cd run_scripts/refcoco nohup sh train_refcoco_el.sh > train_refcoco_el.out & # finetune for refcoco nohup sh train_refcocoplus_el.sh > train_refcocoplus_el.out & # finetune for refcoco+ nohup sh train_refcocog_el.sh > train_refcocog_el.out & # finetune for refcocog </pre> </details> Evaluation is also the same as the default setting.