… the teacher's representation of the data. We formulate this objective as contrastive learning.
Experiments demonstrate that our resulting new objective outperforms knowledge distillation …

… for distillation based on Wasserstein distance, where both global contrastive learning and
… Note that our approach utilizes feature representations at the penultimate layer (before logits…

… contrastive learning, and self distillation. … contrastive learning also considers the contrast
similarity of low-level semantic information between peer residual blocks. Our self distillation …

… proposes to use contrastive tasks as … contrastive loss, our method is more similar to CRD,
but our objective is the mutual relations of deep representations, instead of the representations …

… representation quality affects the distillation performance, we utilize different models to provide
those representations. … -cached keys is referred to as contrastive encoder. The model that …

… Representation). For easier understanding of our framework, we will first briefly review knowledge
distillation and contrastive … self-supervised point cloud representation learning. Finally, …

… distillation efficacy, we propose Contrastive Distillation … distillation framework where the
student is trained to distill knowledge through intermediate layers of the teacher via a contrastive …

… of-the-art voxel-wise representation learning in a more … contrastive voxel-wise representation
distillation framework, which can be utilized to produce superior voxel-wise representations …

… Second, to obtain a general and lightweight feature extractor, we distill the three teacher
models into a small student model by contrastive representation distillation. The distilled model …

… distillation method LRCBERT based on contrastive learning … considered by the existing
distillation methods. Furthermore, … stage training method for the total distillation loss. Finally, by …