Consistency of augmentation graph and network approximability in contrastive learning

arXiv:2502.04312 · cs.LG, math.AP, math.SP · Submitted 2025-02-06 · Read on arXiv

cs.LG, math.AP, math.SP

Submitted: 2025-02-06

Updated: 2026-09-11

Journal ref: Journal of Machine Learning Research 25-1466 2026

Code: https://github.com/jhaochenz96/spectral_contrastive_learning

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Consistency — 22× in this paper · explained in 3 episodes
• Contrastive Learning — 21× in this paper · explained in 5 episodes
• Data Augmentation — 8× in this paper · explained in 7 episodes
• Clustering — 4× in this paper · explained in 2 episodes
• Accuracy — 3× in this paper · explained in 1 episode
• Optimal Transport — 3× in this paper · explained in 5 episodes
• Representation Learning — 3× in this paper · explained in 2 episodes
• Alignment — 2× in this paper · explained in 1 episode
• Generalization — 2× in this paper · explained in 13 episodes
• Ambient Dimension — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Curse of Dimensionality — 1× in this paper · explained in 6 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Graph Construction — 1× in this paper · explained in 3 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Objective Function — 1× in this paper · explained in 2 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Self-Supervised Learning — 1× in this paper · explained in 7 episodes
• Semi-supervised Learning — 1× in this paper · explained in 4 episodes
• Spectral clustering — 1× in this paper · explained in 2 episodes
• Theoretical Guarantees — 1× in this paper · explained in 2 episodes

Sources

• A Theoretical Analysis of Contrastive Unsupervised Representation Learning
• Spectral Neural Networks: Approximation Theory and Optimization Landscape

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