Towards Pointsets Representation Learning via Self-Supervised Learning and Set Augmentation.

Area of Science:

• Computer Science
• Machine Learning
• Data Mining

Background:

• Deep metric learning (DML) is crucial for creating vector spaces that represent complex objects.
• Applying DML to pointsets can optimize retrieval operations and enhance machine learning tasks.
• Existing methods often require extensive labeled data, limiting their application in unsupervised settings.

Purpose of the Study:

• To develop a self-supervised deep metric learning solution specifically for pointsets.
• To leverage Earth's Mover Distance (EMD) for generating pseudo-labels and enhance learning through pointset augmentation.
• To demonstrate the effectiveness of the proposed method in unsupervised settings and its utility as a pre-trained model.

Main Methods:

• A novel self-supervision mechanism utilizing Earth's Mover Distance (EMD) for set ranking and pseudo-label generation.
• A pointset augmentation technique designed to support the self-supervised learning process.
• Experimental validation on diverse datasets including documents, graphs, and point clouds.

Main Results:

• The proposed self-supervised DML method significantly outperforms baseline and state-of-the-art approaches in unsupervised settings.
• The learned representations demonstrate superior performance compared to traditional unsupervised methods.
• The self-supervised model, when fine-tuned, enhances downstream tasks, outperforming current language models.

Conclusions:

• Self-supervised deep metric learning offers a powerful alternative for pointset representation, especially in unsupervised scenarios.
• The integration of EMD and data augmentation provides an effective strategy for label generation and model robustness.
• The developed pre-trained models show promise for boosting performance in various downstream machine learning and data mining applications.