Deep Clustering With Sample-Assignment Invariance Prior.

Area of Science:

• Computer Vision
• Machine Learning
• Data Science

Background:

• Current clustering methods often map raw image data to a projection space for k-means analysis.
• These methods typically rely on specific distance metrics, which can influence clustering outcomes.

Purpose of the Study:

• To propose a novel clustering method based on a newly discovered prior: sample-assignment invariance across different distance metrics.
• To develop an end-to-end clustering approach that jointly optimizes representation and assignment.

Main Methods:

• The proposed method minimizes discrepancies in pairwise sample assignments for each data point.
• It leverages the concept of treating labels as ideal representations to uncover invariance.
• This approach represents one of the first end-to-end clustering techniques.

Main Results:

• The novel method demonstrates remarkable superiority over 16 state-of-the-art clustering techniques.
• Experiments were conducted on five diverse image datasets, validating the method's effectiveness.
• The approach showed significant improvements across four standard evaluation metrics.

Conclusions:

• The discovery of sample-assignment invariance provides a new perspective for image clustering.
• The proposed end-to-end method offers a more robust and effective solution for image clustering tasks.
• This work advances the field by integrating representation learning with clustering in an end-to-end framework.