  Due to the point cloud's irregular and unordered geometry structure,
conventional knowledge distillation technology lost a lot of information when
directly used on point cloud tasks. In this paper, we propose Feature
Adversarial Distillation (FAD) method, a generic adversarial loss function in
point cloud distillation, to reduce loss during knowledge transfer. In the
feature extraction stage, the features extracted by the teacher are used as the
discriminator, and the students continuously generate new features in the
training stage. The feature of the student is obtained by attacking the
feedback from the teacher and getting a score to judge whether the student has
learned the knowledge well or not. In experiments on standard point cloud
classification on ModelNet40 and ScanObjectNN datasets, our method reduced the
information loss of knowledge transfer in distillation in 40x model compression
while maintaining competitive performance.
