I2CKD : Intra- and Inter-Class Knowledge Distillation for Semantic Segmentation
This work addresses the problem of efficient semantic segmentation for applications requiring lightweight models, though it is incremental as it builds on existing knowledge distillation techniques.
The paper tackles improving semantic segmentation performance in compact models by proposing I2CKD, a knowledge distillation method that uses class prototypes and triplet loss to transfer knowledge from teacher to student networks, resulting in enhanced segmentation across datasets like Cityscapes, Pascal VOC, and CamVid.
This paper proposes a new knowledge distillation method tailored for image semantic segmentation, termed Intra- and Inter-Class Knowledge Distillation (I2CKD). The focus of this method is on capturing and transferring knowledge between the intermediate layers of teacher (cumbersome model) and student (compact model). For knowledge extraction, we exploit class prototypes derived from feature maps. To facilitate knowledge transfer, we employ a triplet loss in order to minimize intra-class variances and maximize inter-class variances between teacher and student prototypes. Consequently, I2CKD enables the student to better mimic the feature representation of the teacher for each class, thereby enhancing the segmentation performance of the compact network. Extensive experiments on three segmentation datasets, i.e., Cityscapes, Pascal VOC and CamVid, using various teacher-student network pairs demonstrate the effectiveness of the proposed method.