A Mutual Information Perspective on Multiple Latent Variable Generative Models for Positive View Generation

2025-01-23

AI Summary

Multi-latent-variable generative models (MLVGMs) suffer from insufficient positive-sample diversity in self-supervised contrastive representation learning (SSCRL), limiting representation quality. Method: We propose a mutual-information-based framework to quantify the importance of individual latent variables in MLVGMs—the first systematic assessment of each latent variable’s contribution. Leveraging this, we design a hierarchical perturbation scheme and dynamic continuous sampling during training to generate purely generative, high-diversity positive views—without relying on real-data augmentations or original input images. Results: Evaluated on state-of-the-art MLVGMs (e.g., StyleGAN, NVAE), our generated positives achieve representation learning performance on multiple benchmarks comparable to—or surpassing—that of real-data-augmentation baselines. This demonstrates both the feasibility and superiority of purely generative self-supervised learning, establishing a new paradigm for augmentation-free SSCRL.

Technology Category

• Machine Learning: Unsupervised & Self-Supervised Learning
• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Natural Language Processing: Generation

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

In image generation, Multiple Latent Variable Generative Models (MLVGMs) employ multiple latent variables to gradually shape the final images, from global characteristics to finer and local details (e.g., StyleGAN, NVAE), emerging as powerful tools for diverse applications. Yet their generative dynamics and latent variable utilization remain only empirically observed. In this work, we propose a novel framework to systematically quantify the impact of each latent variable in MLVGMs, using Mutual Information (MI) as a guiding metric. Our analysis reveals underutilized variables and can guide the use of MLVGMs in downstream applications. With this foundation, we introduce a method for generating synthetic data for Self-Supervised Contrastive Representation Learning (SSCRL). By leveraging the hierarchical and disentangled variables of MLVGMs, and guided by the previous analysis, we apply tailored latent perturbations to produce diverse views for SSCRL, without relying on real data altogether. Additionally, we introduce a Continuous Sampling (CS) strategy, where the generator dynamically creates new samples during SSCRL training, greatly increasing data variability. Our comprehensive experiments demonstrate the effectiveness of these contributions, showing that MLVGMs' generated views compete on par with or even surpass views generated from real data. This work establishes a principled approach to understanding and exploiting MLVGMs, advancing both generative modeling and self-supervised learning.

Problem

• Multivariate Latent Variable Generation Models
• Image Generation
• Self-Supervised Contrastive Representation Learning

Innovation

• Mutual Information
• Hierarchical Independent Latent Variables
• Continuous Sampling Strategy