One-Class SVM-guided Negative Sampling for Enhanced Contrastive Learning

Puntos clave

• Contrastive learning improves with one-class SVM-guided negative sampling techniques, enhancing model performance.
• Utilizing negative sampling methods leads to better representation in contrastive learning scenarios.
• Analysis features one-class support vector machine as a key component for selecting informative samples.
• Improving machine learning frameworks may enable more effective data processing and representation tasks.