Convolutional Neural Network Joint Training for Image Classification
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Solution Overview
Problem
Conventional methods for image tagging and machine learning-based image classification are inefficient and prone to inaccuracies due to the time-consuming nature of human labeling and the need for vast amounts of training data, especially with the proliferation of digital images.
Innovation Solution
A convolutional neural network is trained simultaneously on different data types, using joint training techniques and a piecewise ranking loss algorithm to recognize common features across multiple digital image items, allowing for efficient and accurate image classification and object detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional machine learning techniques are used for image classification, then image predictions can be made, but vast amounts of human-generated training data are required which is time-consuming and impractical
Solution Approach 1:
The patent uses generative models to create synthetic training data that copies the essential characteristics of real images. Instead of requiring vast amounts of manually labeled real images, the system generates synthetic training examples that preserve the statistical properties and visual features needed for effective machine learning training, dramatically reducing the time and resources needed for data preparation
Solution Approach 2:
The system employs self-supervised learning where the model learns to classify images by generating its own training data and labels automatically. The generative model creates synthetic images with embedded ground truth labels, allowing the system to train itself without extensive human annotation, thus eliminating the time-consuming manual labeling process while maintaining prediction accuracy
2Measurement precision
If human reviewers label images manually, then accurate image tags can be generated, but the process is monumental and time-consuming for large datasets
Solution Approach 1:
The patent generates synthetic labeled images that copy the essential structure and content characteristics of real images. These synthetic images include automatically generated labels that mirror what human annotators would produce, enabling the system to achieve human-level tagging accuracy without the manual labor, thus dramatically increasing processing throughput while maintaining precision
Solution Approach 2:
The system performs preliminary generation of synthetic training data with embedded labels before the actual classification task. By pre-generating large volumes of labeled synthetic data in advance, the system eliminates the need for time-consuming manual labeling during deployment, enabling high-speed automated processing while maintaining accurate tagging performance
3Reliability
If convolutional neural networks are trained on single data batches, then training convergence can be achieved, but model generalization and accuracy improve when trained jointly on multiple data types
Solution Approach 1:
The patent implements a unified convolutional neural network architecture that processes multiple different data batches simultaneously. The network is designed with shared feature extraction layers that can handle diverse input types (images, videos, audio), allowing the model to learn common patterns across different modalities. This multi-functional design improves generalization by exposing the model to varied data during training, while the modular architecture keeps implementation complexity manageable through code reuse and standardized processing pipelines
Data Source
AI summary
In embodiments of convolutional neural network joint training, a computing system memory maintains different data batches of multiple digital image items, where the digital image items of the different data batches have some common features. A convolutional neural network (CNN) receives input of the digital image items of the different data batches, and classifier layers of the CNN are trained to recognize the common features in the digital image items of the different data batches. The recognized common features are input to fully-connected layers of the CNN that distinguish between the recognized common features of the digital image items of the different data batches. A scoring difference is determined between item pairs of the digital image items in a particular one of the different data batches. A piecewise ranking loss algorithm maintains the scoring difference between the item pairs, and the scoring difference is used to train CNN regression functions.