Hybrid CNN-RNN Evolutionary Training for Large-Scale Data
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Solution Overview
Problem
Traditional optimization methods for deep learning algorithms, such as gradient-based techniques, struggle to handle large-scale datasets, often converging slowly, failing to reach desired accuracy levels, and inadequately exploring the vast search space of possible model parameters.
Innovation Solution
A hybrid neural network architecture combining convolutional neural networks (CNNs) and recurrent neural networks (RNNs), particularly long short-term memory (LSTM) networks, is enhanced using genetic evolutionary algorithms to refine the model structure and parameters, optimizing performance on large digital datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional gradient-based optimization methods are used for deep learning, then the training process is computationally straightforward, but convergence speed is slow and accuracy is insufficient on large-scale datasets
Solution Approach 1:
The patent combines CNN and RNN architectures into a hybrid model that leverages the strengths of both: CNN for spatial feature extraction and RNN for temporal sequence processing. This merged architecture achieves superior accuracy and convergence speed on large-scale datasets compared to either architecture alone
Solution Approach 2:
The system implements self-play training where the neural network plays against itself to generate training data and refine its own performance. This self-service mechanism enables the model to continuously improve accuracy without external intervention, achieving breakthrough performance in complex tasks
2Reliability
If the search space of model parameters is expanded to improve accuracy, then better performance is achieved, but the complexity of optimization increases significantly
Solution Approach 1:
The patent segments the optimization process into distinct phases: CNN training for spatial features, RNN training for temporal features, and hybrid joint training. This segmentation breaks down the complex optimization of the entire hybrid model into manageable stages, reducing optimization complexity while maintaining accuracy
Solution Approach 2:
The system dynamically adjusts hyperparameters such as learning rate, batch size, and network depth based on training progress and performance metrics. These parameter changes enable the model to navigate the vast search space efficiently, achieving high accuracy without getting trapped in local optima
3Adaptability or versatility
If hybrid CNN-RNN architecture is used to process multi-modal data, then processing capability is enhanced, but computational resources required increase
Solution Approach 1:
The patent extracts and processes different data modalities through specialized pathways: CNN processes spatial data (images, video frames) while RNN processes sequential data (text, time series). This extraction approach allows each component to focus on its strength, improving overall capability while optimizing resource utilization compared to a monolithic architecture
Data Source
AI summary
Embodiments of the present disclosure relate to neural networks for processing large digital datasets. Neural networks comprise both a convolutional neural network (CNN) and a recurrent neural network (RNN). The neural networks are optimized by applying genetic algorithms. Embedding vectors are processed by both the CNN and the RNN to produce a merged output.