… knowledge distillation for DTA tasks as an improvement in training strategy. Knowledge
distillation … knowledge from the teacher model with more parameters. Through transferring …

… introduced a novel knowledge-distillation insights drug-target affinity prediction model with
… and explainable predictions. We benchmarked the model on public affinity prediction and …

… on knowledge distillation focus on classification tasks [10, 11, 15, 26, 35, 37, 43]. Our work
here aims to study efficient and effective distillation methods for dense prediction, … affinity map. …

… knowledge from a 3Dteacher network to the 2D student network by minimizingthe affinity
prediction … By incorporatingonline knowledge distillation, our method further enhancesthe …

… affinity attention map to give a prior prediction of query target. Based on the two modules
mentioned above, we propose the Self-Distillation embedded Affinity … the knowledge distillation …

… predicted maps in the spatial domain, but channel distillation also may help to improve
segmentation performance. Additionally, pairwise pixel affinity … a novel Channel Affinity KD (CAKD…

… ) and compared their prediction performances without the self-distillation module. …
distillation module by analyzing the prediction performance of Layer_1 with KD (with the self-distillation …

… Affinity value corresponds to smaller offset between the data distributions. In this paper, Affinity
… of data augmentation on the student’s prediction confidence and model calibration in KD …

… task-specific guidance to enable effective knowledge transfer. In Figure 1, we show the … ’
prediction when using KL divergence (see Figure 1). We propose a novel knowledge distillation …

… We study the problem of distilling knowledge from a large deep … In this work, we explore
a novel knowledge distillation (KD) ap… Apart from model predictions, we also show the deep …