Multi-Channel Feature Extraction with Bottleneck Parameter Reduction
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Solution Overview
Problem
Neural networks face challenges with overfitting and increased computational costs due to high parameter counts during feature extraction, particularly in processing multi-channel input data from systems like MIMO radar, affecting their effectiveness and accuracy.
Innovation Solution
Implementing a bottleneck-based block with a squeeze and excitation (SE) block in a neural network path to reduce parameters, followed by upsampling and concatenation of features to maintain input size, utilizing pointwise and depthwise convolution operations without activation functions to enhance efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a neural network uses a large number of parameters for feature extraction from multi-channel input data, then the feature extraction capability is improved, but overfitting occurs and computational costs increase
Solution Approach 1:
The neural network is divided into two distinct paths: a first type of path that processes features through bottleneck-based blocks for parameter reduction, and a second type of path that preserves feature quality. This segmentation allows each path to specialize in different aspects of feature extraction, resolving the contradiction between accuracy and overfitting.
Solution Approach 2:
The bottleneck-based block dynamically changes parameters by reducing the dimensionality of features through sequential processing stages. The block transforms high-dimensional input features into lower-dimensional representations, thereby reducing the total parameter count while maintaining essential feature information for accurate extraction.
2Measurement precision
If a neural network uses a large number of parameters for feature extraction, then feature extraction capability is improved, but computational costs and memory requirements increase
Solution Approach 1:
By segmenting the network into two paths with different processing strategies, the computational burden is distributed efficiently. The first path handles parameter reduction with bottleneck blocks, while the second path focuses on feature preservation, improving overall computational efficiency without sacrificing accuracy.
Solution Approach 2:
The bottleneck-based block extracts only the most essential feature information by reducing dimensionality. This extraction process removes redundant parameters and computations, thereby improving computational efficiency while retaining the critical features needed for accurate extraction.
3Productivity
If parameters are reduced in the first type of path, then computational efficiency is improved, but feature quality may deteriorate
Solution Approach 1:
The outputs from both the first type of path (with reduced parameters) and the second type of path (with preserved feature quality) are merged or combined. This merging allows the network to benefit from both computational efficiency and high feature quality, resolving the contradiction between the two objectives.
Solution Approach 2:
The second type of path acts as an intermediary that preserves feature quality and compensates for any information loss in the first path. By introducing this intermediary path, the network maintains feature quality while still achieving computational efficiency through the bottleneck-based parameter reduction.
Data Source
AI summary
A feature extraction method is provided. The feature extraction method includes applying a first feature extracted from multi-channel input data to a bottleneck-based block included in a first type of path of a neural network and obtaining a second feature including a reduced parameter compared to the first feature, upsampling a derived feature of the second feature obtained based on a layer included in a second type of path of the neural network to correspond to a size of a derived feature of the first feature, and obtaining an intermediate feature applied to a head for a task of the neural network, based on the upsampled derived feature of the second feature and the derived feature of the first feature.