Pixelated Encoder Matching with Supplemental Pixel Augmentation
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Solution Overview
Problem
Existing machine learning models are insufficiently accurate in matching disparate datasets, leading to inaccurate identification of related data across different data repositories.
Innovation Solution
A pixelated encoder machine learning model is trained using multimodal convolutional layers and decoding networks to identify missing pixels, generate supplemental pixels, and retrain the model to enhance accuracy in matching disparate datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing machine learning models are used to match disparate datasets, then the matching process can be performed, but the accuracy of identifying related datasets is insufficient
Solution Approach 1:
The patent applies preliminary action by performing data augmentation before the matching process. Missing pixels in datasets are identified and filled using generative models (GANs) to create augmented versions of the datasets. This preliminary completion of incomplete data structures improves the accuracy and reliability of subsequent matching operations between disparate datasets.
Solution Approach 2:
The patent introduces an intermediary mechanism in the form of a pixel-level attention module and generative model that acts as a mediator between disparate datasets. This intermediary component generates supplemental pixel information and facilitates the matching process by bridging gaps between different data formats and structures, thereby improving identification accuracy.
2Measurement precision
If data structures are augmented with supplemental pixels, then the accuracy of matching improves, but the complexity of the processing system increases
Solution Approach 1:
The patent applies segmentation by breaking down the complex task of matching disparate datasets into distinct processing stages: (1) identifying missing pixels in source and target datasets, (2) generating supplemental pixels using specialized generative models, (3) augmenting data structures with the generated pixels, and (4) performing the matching operation. This segmentation manages system complexity by organizing operations into modular, sequential steps.
Solution Approach 2:
The patent changes parameters by transforming incomplete data structures into augmented versions through pixel generation. The system modifies the state of data structures by adding supplemental pixel information, changing the parameters of the input data to improve matching accuracy while managing processing complexity through controlled transformation.
Data Source
AI summary
A method including using a set of machine learning models to identify a source dataset that matches a target dataset. The source and target datasets are received as a source and target image data structures. A set of multimodal convolutional layers of encoding networks are applied to the source and target image data structures to generate classes of data. Missing pixels that are missing in at least one of the source and target image data structures are identified. Supplemental pixels corresponding to the missing pixels are generated from text present in at least one of the source and target datasets. At least one of the source and target image data structures are augmented with the supplemental pixels to generate at least one enhanced image. The method also includes retraining, using an augmented data structure including the at least one enhanced image, the encoding and decoding networks.