Neural Network Pretraining With Unpaired Image-Text Data
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Solution Overview
Problem
Training neural networks is challenging due to the difficulty in obtaining suitable training data, particularly for tasks involving images and associated annotations.
Innovation Solution
A pre-training framework that utilizes a mix of paired and unpaired data to train neural networks using transformer-based models, employing multi-scale masked vision modeling and cross-correlation modules to learn joint representations of image and text data in a self-supervised manner.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional training methods are used with paired image-text data, then training accuracy can be improved, but data acquisition difficulty and time consumption increase significantly
Solution Approach 1:
The patent applies preliminary action by pre-training the neural network on large-scale unpaired image-text datasets before fine-tuning on smaller paired datasets. This preliminary pre-training phase enables the model to learn general image-text representations in advance, reducing the time and data required for subsequent task-specific training while maintaining high accuracy.
Solution Approach 2:
The patent introduces an intermediary mechanism by using a dual-encoder architecture with separate image and text encoders that are initially trained independently on unpaired data. These intermediaries (independent encoders) are later combined and fine-tuned together, allowing the system to leverage both unpaired and paired data effectively while reducing overall training time.
2Reliability
If more training data is collected to improve model performance, then training effectiveness increases, but data processing complexity and computational resources increase
Solution Approach 1:
The patent applies segmentation by dividing the training process into distinct phases: pre-training on unpaired data and fine-tuning on paired data. It also segments the data into unpaired image-text sets and paired image-text sets, processing them through different training protocols. This segmentation reduces overall processing complexity while maintaining model performance.
Solution Approach 2:
The patent applies partial action by using a subset of paired data for fine-tuning after comprehensive pre-training on unpaired data. The model achieves sufficient performance with partial paired training data because the pre-training phase has already established strong general representations, reducing the need for extensive paired data processing.
3Productivity
If paired image-text training data is used, then training speed can be improved, but data availability and acquisition ease worsen
Solution Approach 1:
The patent applies universality by designing a training framework that can process both unpaired and paired data through the same neural network architecture. The system is multi-functional, capable of performing pre-training on easily acquired unpaired data and fine-tuning on paired data, making the overall system adaptable to different data availability scenarios while maintaining efficient training.
Solution Approach 2:
The patent applies preliminary action by conducting pre-training on unpaired data before fine-tuning on paired data. This preliminary phase enables the model to develop robust features from readily available unpaired data, after which paired data is used only for task-specific adaptation, significantly improving overall training efficiency.
Data Source
AI summary
Apparatuses, systems, and techniques to indicate an extent, to which text corresponds to one or more images. In at least one embodiment, an extent to which text corresponds to one or more images is indicated using one or more neural networks and used to train the one or more neural networks.