Structured Contrastive Learning for Interpretable Latent Representations

2025-11-18

AI Summary

Neural networks exhibit high sensitivity to semantically irrelevant transformations—such as ECG phase shifts or IMU sensor rotations—leading to representation degradation and performance collapse. To address this, we propose a structured contrastive learning framework that pioneers the disentanglement of latent representations into three functionally distinct subspaces: invariant (encoding semantics), variant (modeling controlled transformations), and free (capturing residual variability). This design jointly ensures semantic invariance and explicit modeling of admissible variations, unifying robustness with interpretability. Our method requires no architectural modifications; instead, it achieves end-to-end structural learning via semantic grouping and embedded training, advancing contrastive learning from passive data augmentation toward active structural modeling. Evaluated on ECG phase-invariance tasks, our approach achieves a similarity score of 0.91 (+0.66 improvement); on IMU pose-robust activity recognition, it attains 86.65% accuracy and 95.38% rotation consistency.

Technology Category

• Machine Learning: Structured Learning
• Computer Vision: Representation Learning for Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Neural networks exhibit severe brittleness to semantically irrelevant transformations. A mere 75ms electrocardiogram (ECG) phase shift degrades latent cosine similarity from 1.0 to 0.2, while sensor rotations collapse activity recognition performance with inertial measurement units (IMUs). We identify the root cause as"laissez-faire"representation learning, where latent spaces evolve unconstrained provided task performance is satisfied. We propose Structured Contrastive Learning (SCL), a framework that partitions latent space representations into three semantic groups: invariant features that remain consistent under given transformations (e.g., phase shifts or rotations), variant features that actively differentiate transformations via a novel variant mechanism, and free features that preserve task flexibility. This creates controllable push-pull dynamics where different latent dimensions serve distinct, interpretable purposes. The variant mechanism enhances contrastive learning by encouraging variant features to differentiate within positive pairs, enabling simultaneous robustness and interpretability. Our approach requires no architectural modifications and integrates seamlessly into existing training pipelines. Experiments on ECG phase invariance and IMU rotation robustness demonstrate superior performance: ECG similarity improves from 0.25 to 0.91 under phase shifts, while WISDM activity recognition achieves 86.65% accuracy with 95.38% rotation consistency, consistently outperforming traditional data augmentation. This work represents a paradigm shift from reactive data augmentation to proactive structural learning, enabling interpretable latent representations in neural networks.

Problem

• Neural networks are brittle to semantically irrelevant transformations like ECG phase shifts
• Unconstrained latent space evolution causes performance collapse under sensor rotations
• Current representation learning lacks interpretable feature grouping for robustness

Innovation

• Structured Contrastive Learning partitions latent representations semantically
• Variant features differentiate transformations within positive contrastive pairs
• Framework requires no architectural changes to existing training pipelines