Joint graph entropy knowledge distillation for point cloud classification and robustness against corruptions
This addresses classification accuracy and robustness in 3D point clouds, which is important for applications like robotics and autonomous driving, but appears incremental as it builds on existing knowledge distillation techniques.
The paper tackles the problem of 3D point cloud classification under non-IID assumptions by proposing JGEKD, a method using joint graph entropy knowledge distillation to capture class correlations and improve robustness against corruptions, achieving competitive results on datasets like ScanObject and ModelNet40.
Classification tasks in 3D point clouds often assume that class events \replaced{are }{follow }independent and identically distributed (IID), although this assumption destroys the correlation between classes. This \replaced{study }{paper }proposes a classification strategy, \textbf{J}oint \textbf{G}raph \textbf{E}ntropy \textbf{K}nowledge \textbf{D}istillation (JGEKD), suitable for non-independent and identically distributed 3D point cloud data, \replaced{which }{the strategy } achieves knowledge transfer of class correlations through knowledge distillation by constructing a loss function based on joint graph entropy. First\deleted{ly}, we employ joint graphs to capture add{the }hidden relationships between classes\replaced{ and}{,} implement knowledge distillation to train our model by calculating the entropy of add{add }graph.\replaced{ Subsequently}{ Then}, to handle 3D point clouds \deleted{that is }invariant to spatial transformations, we construct \replaced{S}{s}iamese structures and develop two frameworks, self-knowledge distillation and teacher-knowledge distillation, to facilitate information transfer between different transformation forms of the same data. \replaced{In addition}{ Additionally}, we use the above framework to achieve knowledge transfer between point clouds and their corrupted forms, and increase the robustness against corruption of model. Extensive experiments on ScanObject, ModelNet40, ScanntV2\_cls and ModelNet-C demonstrate that the proposed strategy can achieve competitive results.