Structurally Regularized CNN for Pattern Recognition
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Solution Overview
Problem
Deep neural networks (DNNs) and convolutional neural networks (CNNs) face challenges such as high computational costs and overfitting, especially as dataset sizes increase, leading to difficulties in capturing complex spatiotemporal dependencies and improving efficiency, training time, and performance.
Innovation Solution
The proposed solution involves an apparatus and method for pattern recognition using a structurally regularized CNN architecture. This is achieved by decomposing input data into sub-bands, processing each sub-band independently with a respective CNN, aggregating the outputs, and training the CNNs to recognize patterns. This approach emphasizes regularization through the network's structure, inhibiting CNNs from training on information from other sub-bands and applying weight regularization within each CNN.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the size of training datasets increases to capture complex patterns, then the degree of non-linearity increases exponentially, but this makes it more difficult to capture complex spatiotemporal dependencies and impacts efficiency and training time
Solution Approach 1:
The patent divides the training process into multiple stages: pre-training on smaller datasets to learn basic features, then fine-tuning on larger datasets. This segmentation allows the network to efficiently capture complex patterns without the exponential computational cost of training large models on large datasets simultaneously.
Solution Approach 2:
The patent performs pre-training on smaller datasets before fine-tuning on larger datasets. This preliminary action allows the network to learn fundamental features efficiently, reducing the computational burden when subsequently training on larger datasets with higher non-linearity.
2Measurement precision
If DNNs/CNNs require several layers of weights to be trained to capture complex representations, then the model capacity increases, but they become vulnerable to overfitting and the curse of dimensionality
Solution Approach 1:
The patent segments the training process into pre-training and fine-tuning phases, and also divides the network into layers that are trained sequentially. This prevents overfitting by allowing each layer to learn progressively complex features without being exposed to the full complexity of the entire dataset simultaneously.
Solution Approach 2:
The patent applies partial training by first training only certain layers on smaller datasets, then gradually introducing more layers and larger datasets. This controlled progression prevents the network from overfitting while still capturing complex representations.
3Measurement precision
If larger models are trained on larger datasets to improve deep semantic understanding, then the computational cost and memory requirements increase significantly
Solution Approach 1:
The patent segments both the model and the training process. The network is divided into layers trained in sequence, and the dataset is effectively segmented into smaller pre-training sets and larger fine-tuning sets. This reduces memory requirements and computational cost compared to loading and processing entire large datasets simultaneously.
Solution Approach 2:
The patent performs preliminary training on smaller datasets before scaling to larger datasets. This preliminary action reduces the overall computational cost by pre-learning features that can be transferred, avoiding the need to process all data from scratch.
Data Source
AI summary
Machine learning architectures to perform pattern recognition such as a structurally regularized convolutional neural network architecture, along with corresponding methods of operation, are provided. One such architecture includes a memory, and a processor coupled to the memory, where the processor receives data including a pattern to be recognized, decomposes the data into of sub-bands, and processes each of the sub-bands with a respective convolutional neural network (CNN) to generate outputs, where each of the CNNs operates independently of the other CNNs. The processor aggregates the outputs of the CNNs, and trains, using the aggregated output, the CNNs to recognize the pattern.