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Efficient Point Cloud Classification via Offline Distillation Framework and Negative-Weight Self-Distillation Technique

Qiang Zheng, Chao Zhang, Jian Sun

TL;DR

An innovative offline recording strategy that avoids the simultaneous loading of both teacher and student models, thereby reducing hardware demands and incorporating a negative-weight self-distillation strategy is introduced, providing a novel solution for efficient point cloud analysis.

Abstract

The rapid advancement in point cloud processing technologies has significantly increased the demand for efficient and compact models that achieve high-accuracy classification. Knowledge distillation has emerged as a potent model compression technique. However, traditional KD often requires extensive computational resources for forward inference of large teacher models, thereby reducing training efficiency for student models and increasing resource demands. To address these challenges, we introduce an innovative offline recording strategy that avoids the simultaneous loading of both teacher and student models, thereby reducing hardware demands. This approach feeds a multitude of augmented samples into the teacher model, recording both the data augmentation parameters and the corresponding logit outputs. By applying shape-level augmentation operations such as random scaling and translation, while excluding point-level operations like random jittering, the size of the records is significantly reduced. Additionally, to mitigate the issue of small student model over-imitating the teacher model's outputs and converging to suboptimal solutions, we incorporate a negative-weight self-distillation strategy. Experimental results demonstrate that the proposed distillation strategy enables the student model to achieve performance comparable to state-of-the-art models while maintaining lower parameter count. This approach strikes an optimal balance between performance and complexity. This study highlights the potential of our method to optimize knowledge distillation for point cloud classification tasks, particularly in resource-constrained environments, providing a novel solution for efficient point cloud analysis.

Efficient Point Cloud Classification via Offline Distillation Framework and Negative-Weight Self-Distillation Technique

TL;DR

An innovative offline recording strategy that avoids the simultaneous loading of both teacher and student models, thereby reducing hardware demands and incorporating a negative-weight self-distillation strategy is introduced, providing a novel solution for efficient point cloud analysis.

Abstract

The rapid advancement in point cloud processing technologies has significantly increased the demand for efficient and compact models that achieve high-accuracy classification. Knowledge distillation has emerged as a potent model compression technique. However, traditional KD often requires extensive computational resources for forward inference of large teacher models, thereby reducing training efficiency for student models and increasing resource demands. To address these challenges, we introduce an innovative offline recording strategy that avoids the simultaneous loading of both teacher and student models, thereby reducing hardware demands. This approach feeds a multitude of augmented samples into the teacher model, recording both the data augmentation parameters and the corresponding logit outputs. By applying shape-level augmentation operations such as random scaling and translation, while excluding point-level operations like random jittering, the size of the records is significantly reduced. Additionally, to mitigate the issue of small student model over-imitating the teacher model's outputs and converging to suboptimal solutions, we incorporate a negative-weight self-distillation strategy. Experimental results demonstrate that the proposed distillation strategy enables the student model to achieve performance comparable to state-of-the-art models while maintaining lower parameter count. This approach strikes an optimal balance between performance and complexity. This study highlights the potential of our method to optimize knowledge distillation for point cloud classification tasks, particularly in resource-constrained environments, providing a novel solution for efficient point cloud analysis.
Paper Structure (16 sections, 4 equations, 3 figures, 6 tables)

This paper contains 16 sections, 4 equations, 3 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Related works
  3. Point Cloud Analysis
  4. Knowledge Distillation
  5. Methodology
  6. Offline Distillation Framework
  7. Negative-Weight Self-Distillation
  8. Network Configuration
  9. Experiments
  10. ModelNet40 Classification
  11. Complexity Analysis
  12. Ablation Experiments on Framework Design
  13. Effects of Distillation Weights on Model Performance:
  14. Visualization Analysis of Encoder Features
  15. Visualization Analysis of Logit Outputs
  16. ...and 1 more sections

Figures (3)

  • Figure 1: The figure depicts the two-stage offline distillation framework, beginning with a pre-trained teacher model that infers input samples and captures soft labels and data augmentation parameters. The subsequent phase trains a student model utilizing the offline record for teacher-student distillation, enhanced by the introduction of negative weight self-distillation. The overall architecture encompasses three types of loss functions: classification, teacher-student distillation, and self-distillation.
  • Figure 2: t-SNE visualization of encoded features for (a) PointViG 2024PointViG (teacher model), (b) PointViG-Distil (no distillation), (c) PointViG-Distil (teacher-student distillation only), and (d) PointViG-Distil (teacher-student and negative-weight self-distillation). This figure is best viewed in an enlarged format for clarity.
  • Figure 3: t-SNE visualization of encoded features for (a) PointViG 2024PointViG (teacher model), (b) PointViG-Distil (no distillation), (c) PointViG-Distil (teacher-student distillation only), and (d) PointViG-Distil (teacher-student and negative-weight self-distillation). Ten representative regions are consistently labeled across all plots for comparison of confidence levels and decision boundaries. This figure is best viewed in an enlarged format for clarity.