Contrastive Learning with Self-Label Refinement for Noisy Negatives
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Solution Overview
Problem
Contrastive learning methods in self-supervised learning often generate inaccurate and uninformative one-hot labels due to semantically similar negatives, leading to performance degradation in downstream tasks.
Innovation Solution
Implement a contrastive learning mechanism with self-labeling refinement that iteratively generates more accurate and informative soft labels by combining estimated similarity with one-hot labels, and use a momentum mix-up module to reduce label noise and increase augmentation diversity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If contrastive learning uses one-hot labels for instance discrimination, then the training process is simple and fast, but the label accuracy is low and uninformative
Solution Approach 1:
The system uses the network itself to generate self-labels by predicting semantic labels from augmented views of the same instance. The network leverages its own learned representations to create more accurate labels without external annotation, resolving the contradiction between label accuracy and system complexity
Solution Approach 2:
The system implements iterative refinement where the network uses its current predictions to generate improved labels, which then feedback into subsequent training iterations. This feedback loop progressively enhances label accuracy while maintaining manageable complexity through controlled iteration
2Reliability
If contrastive learning pulls positive instances close and pushes negatives away, then instance discrimination is achieved, but semantically similar negatives cause performance degradation
Solution Approach 1:
The system changes the labeling parameter from hard one-hot labels to soft self-generated labels that encode semantic similarity information. This parameter change allows the model to retain information about semantic relationships between instances while still achieving instance discrimination
Solution Approach 2:
The system introduces self-generated semantic labels as an intermediary between the raw instance data and the contrastive learning objective. This intermediary preserves semantic similarity information that would otherwise be lost in hard one-hot labeling, while still enabling effective instance discrimination
Data Source
AI summary
Embodiments described herein provide a contrastive learning mechanism with self-labeling refinement, which iteratively employs the network and data themselves to generate more accurate and informative soft labels for contrastive learning. Specifically, the contrastive learning framework includes a self-labeling refinery module to explicitly generate accurate labels, and a momentum mix-up module to increase similarity between a query and its positive, which in turn implicitly improves label accuracy.