Neural Network Layer Pruning via Probing Neurons
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Solution Overview
Problem
Current methods for determining the optimal structure of multilayer neural networks, particularly the number of layers, are heuristic and do not effectively optimize the number of layers for high generalization capability, leading to increased processing load and computation time.
Innovation Solution
A learning apparatus and method that includes a layer quantity adjusting unit to remove unnecessary layers based on the output from probing neurons, optimizing the number of layers in a multilayer neural network through a cost function, and subsequent relearning processes to minimize cost and improve discriminative capability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the number of layers in a multilayer neural network is increased to improve discriminative capability, then the discriminative capability is improved, but the calculation amount and processing time increase significantly
Solution Approach 1:
The patent applies dynamics by making the network structure adjustable during the learning process. The number of layers is not fixed but can be dynamically modified based on learning progress. The system automatically adds or removes layers depending on whether the cost function is decreasing or increasing, allowing the network to adapt its complexity to the task requirements rather than being statically configured.
Solution Approach 2:
The patent changes the structural parameter of the network (number of layers) during the learning process. When the cost function begins to increase, the system reduces the number of layers by removing the uppermost layer. This parameter change allows the network to maintain optimal complexity, preventing unnecessary computational overhead while preserving discriminative capability.
2Measurement precision
If the number of layers is manually set to optimize discriminative capability, then the structure is fixed, but the processing load increases and computation time is extended
Solution Approach 1:
The patent implements self-service by enabling the neural network to automatically adjust its own structure during the learning process. The system monitors the cost function and autonomously decides when to add or remove layers without external intervention. This self-adjusting mechanism eliminates the need for manual structure optimization and prevents unnecessary computational burden by maintaining only the required number of layers.
Solution Approach 2:
The patent uses feedback from the cost function to control structural modifications. When the cost function begins to increase, this feedback triggers the removal of the uppermost layer. This feedback mechanism ensures that the network maintains optimal complexity by responding to performance degradation signals, thereby reducing processing load while preserving discriminative capability.
3Adaptability or versatility
If heuristic methods are used to determine network structure, then the structure can be obtained, but there is no theoretical basis for optimal generalization capability
Solution Approach 1:
The patent replaces heuristic methods with a feedback-driven approach based on the cost function. Instead of relying on theoretical assumptions or empirical rules, the system uses actual performance feedback (cost function values) to determine structural modifications. This provides a reliable, data-driven basis for optimizing generalization capability rather than relying on unproven theoretical frameworks.
Solution Approach 2:
The patent transitions from static heuristic methods to dynamic, performance-based structure determination. The network structure evolves during learning based on actual cost function behavior rather than predetermined theoretical guidelines. This dynamic adaptation provides a practical, reliable basis for achieving good generalization capability through empirical performance rather than theoretical assumptions.
Data Source
AI summary
A learning apparatus performs a learning process for a feed-forward multilayer neural network with supervised learning. The network includes an input layer, an output layer, and at least one hidden layer having at least one probing neuron that does not transfer an output to an uppermost layer side of the network. The learning apparatus includes a learning unit and a layer quantity adjusting unit. The learning unit performs a learning process by calculation of a cost derived by a cost function defined in the multilayer neural network using a training data set for supervised learning. The layer quantity adjusting unit removes at least one uppermost layer from the network based on the cost derived by the output from the probing neuron, and sets, as the output layer, the probing neuron in the uppermost layer of the remaining layers.