Neural Network Training With Pseudo-Labels and Uncertainty Ranking
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Solution Overview
Problem
Training neural networks with labeled data is challenging due to the difficulty in data labeling, which affects accuracy.
Innovation Solution
A cold start for active learning is employed, where neural networks are trained using an entirely unlabeled dataset, generating pseudo-labels through a proxy task, and ranking data by uncertainty to prioritize labeling efforts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If neural networks are trained with labeled data to improve accuracy, then prediction accuracy is improved, but the difficulty and time required for data labeling increases
Solution Approach 1:
The system uses a proxy model to automatically generate pseudo-labels for unlabeled data, enabling the system to self-label data without human intervention. The proxy model processes unlabeled data and generates predictions that are then used to train the main neural network, significantly reducing the time and resources required for manual labeling while maintaining training accuracy.
Solution Approach 2:
A proxy model serves as an intermediary between the unlabeled data and the main neural network. The proxy model first processes the unlabeled data to generate pseudo-labels, which then serve as training inputs for the main neural network. This intermediary approach allows the system to leverage unlabeled data effectively without requiring direct human annotation.
2Productivity
If more unlabeled data is used to reduce labeling costs, then labeling efficiency is improved, but the complexity of handling and processing unlabeled data increases
Solution Approach 1:
The system segments the data processing task into two distinct stages: first, the proxy model generates pseudo-labels for unlabeled data; second, the main neural network uses these pseudo-labels for training. This segmentation allows the system to handle large volumes of unlabeled data systematically, breaking down the complex processing into manageable steps that improve both efficiency and scalability.
Data Source
AI summary
Apparatuses, systems, and techniques to identify objects within an image. In at least one embodiment, objects are identified in an image using one or more neural networks based, at least in part, on neural network outputs ranked according to uncertainty values.