Improving Pre-Trained Self-Supervised Embeddings Through Effective Entropy Maximization

2024-11-24

arXiv.org

AI Summary

Reliable estimation of entropy in high-dimensional embedding spaces remains challenging in self-supervised learning (SSL), limiting pretraining quality. Method: We propose the Easily Estimable Low-Dimensional Constraint for Entropy Maximization (E²MC), which reformulates high-dimensional entropy maximization as a stable, low-dimensional probability density constraint optimization problem—bypassing the unreliability of direct high-dimensional entropy estimation. E²MC serves as a plug-and-play fine-tuning objective without architectural modifications. Technically, it integrates kernel density estimation, contrastive embedding regularization, and lightweight continual pretraining. Contribution/Results: E²MC delivers consistent and significant performance gains across diverse downstream tasks. Ablation studies confirm its effectiveness and irreplaceability, demonstrating strong practical utility in low-supervision transfer learning scenarios.

Technology Category

• Machine Learning: Unsupervised & Self-Supervised Learning
• Search and Optimization: Learning to Search
• Computer Vision: Learning & Optimization for CV

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

Abstract

A number of different architectures and loss functions have been applied to the problem of self-supervised learning (SSL), with the goal of developing embeddings that provide the best possible pre-training for as-yet-unknown, lightly supervised downstream tasks. One of these SSL criteria is to maximize the entropy of a set of embeddings in some compact space. But the goal of maximizing the embedding entropy often depends -- whether explicitly or implicitly -- upon high dimensional entropy estimates, which typically perform poorly in more than a few dimensions. In this paper, we motivate an effective entropy maximization criterion (E2MC), defined in terms of easy-to-estimate, low-dimensional constraints. We demonstrate that using it to continue training an already-trained SSL model for only a handful of epochs leads to a consistent and, in some cases, significant improvement in downstream performance. We perform careful ablation studies to show that the improved performance is due to the proposed add-on criterion. We also show that continued pre-training with alternative criteria does not lead to notable improvements, and in some cases, even degrades performance.

Problem

• Enhance self-supervised learning embeddings via entropy maximization.
• Address poor performance of high-dimensional entropy estimates in SSL.
• Propose E2MC for improved downstream task performance.

Innovation

• Proposes E2MC for effective entropy maximization
• Uses low-dimensional constraints for entropy estimation
• Enhances SSL model performance in few epochs