Multi-Model Feature Clustering to Reduce Training-Data Labeling
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Solution Overview
Problem
The availability of labeled training data in sufficient quantity and diversity is a significant limiting factor for training new machine learning models, particularly in tasks like computer vision, where image acquisition and manual labeling are costly and time-consuming, and existing methods that reduce label requirements often fail to build a global view of diverse perspectives.
Innovation Solution
Utilizing multiple pre-trained models to analyze unlabeled data, organizing it into clusters with high probability of belonging to the same class, and applying graph-based measures to automatically annotate large datasets with a fraction of manually labeled samples, leveraging embeddings from various layer depths and cross-embedding analysis to assign pseudo-labels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labeling is used to ensure data quality and diversity, then labeling accuracy is improved, but time consumption and cost increase significantly
Solution Approach 1:
The system performs preliminary clustering and pseudo-labeling on unlabeled data before final human verification. By pre-organizing data into clusters and assigning pseudo-labels automatically, the system reduces the subsequent human labeling workload while maintaining quality, directly addressing the time-cost tradeoff
Solution Approach 2:
The system introduces an intermediary automated labeling process between raw unlabeled data and final human-labeled data. This intermediary step uses pre-trained models to generate pseudo-labels that serve as a bridge, reducing the burden on human annotators while preserving accuracy
2Extent of automation
If multiple pre-trained models are used to analyze unlabeled data, then automatic annotation capability is improved, but computational complexity increases
Solution Approach 1:
The system segments the complex task of automatic annotation into multiple independent stages: embedding extraction from multiple pre-trained models, clustering based on embedding similarity, and pseudo-label assignment. This segmentation allows each component to be optimized independently and reduces overall computational complexity
Solution Approach 2:
The system uses multiple pre-trained models that can serve different functions simultaneously - some models are better at certain types of clustering while others excel at specific feature extraction. This multi-functionality allows the system to handle diverse data types and clustering requirements without requiring completely separate systems for each task
Data Source
AI summary
A method of labeling training data includes inputting a plurality of unlabeled input data samples into each of a plurality of pre-trained neural networks and extracting a set of feature embeddings from multiple layer depths of each of the plurality of pre-trained neural networks. The method also includes generating a plurality of clusterings from the set of feature embeddings. The method also includes analyzing, by a processing device, the plurality of clusterings to identify a subset of the plurality of unlabeled input data samples that belong to a same unknown class. The method also includes assigning pseudo-labels to the subset of the plurality of unlabeled input data samples.