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This study explores self-supervised learning (SSL) using Tree-Wasserstein distance (TWD). We found that combining TWD with specific probability models and Jeffrey divergence regularization stabilizes training and improves performance over cosine similarity.

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Area of Science:

  • Machine Learning
  • Computer Vision

Background:

  • Self-supervised learning (SSL) commonly uses cosine similarity.
  • Wasserstein distance, specifically Tree-Wasserstein distance (TWD), is less explored in SSL.
  • Training Wasserstein distance can be numerically challenging.

Purpose of the Study:

  • Investigate optimizing SSL with TWD.
  • Identify stable training procedures for TWD in SSL.
  • Evaluate TWD's effectiveness against cosine similarity in representation learning.

Main Methods:

  • Employed two TWD types: total variation and ClusterTree.
  • Tested various probability models: softmax, ArcFace, and simplicial embedding.
  • Introduced Jeffrey divergence-based regularization for optimization stability.

Main Results:

  • A softmax and TWD combination outperformed standard SimCLR on benchmark datasets (STL10, CIFAR10/100, SVHN).
  • TWD with SimSiam failed to train, indicating sensitivity to model combinations.
  • Jeffrey divergence regularization significantly aided model training.

Conclusions:

  • Model performance in SSL with TWD is highly dependent on the chosen probability model.
  • TWD, when appropriately combined with probability models and regularization, surpasses cosine similarity-based methods for representation learning.