Shift-Resilient Neural Network Feature Maps via Low-Pass Filtering
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Solution Overview
Problem
Conventional neural network systems suffer from inaccuracies, inflexibility, and inefficiency due to lack of shift-invariance, requiring invasive modifications and significant resources for data augmentation and training.
Innovation Solution
Implementing a low-pass filtering technique after densely pooling feature maps within neural networks to generate shift-equivariant outputs, reducing the need for shift-based data augmentation and allowing adaptation to original pooling layers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional pooling layers are used in neural networks, then the network structure is simple and easy to implement, but the network loses shift-invariance and generates incorrect outputs for shifted inputs
Solution Approach 1:
The pooling operation is segmented into two distinct stages: first, a conventional pooling layer performs standard pooling to reduce computational complexity; second, a separate post-pooling adjustment layer applies shift compensation based on detected shift parameters. This segmentation allows the system to maintain simplicity while achieving shift-invariance through the added adjustment stage.
Solution Approach 2:
An intermediary shift detection and compensation mechanism is introduced between the conventional pooling layer and subsequent network layers. This intermediary component detects shifts in pooled features and applies appropriate compensation transformations, acting as a mediator that preserves shift-invariance without requiring complete redesign of the pooling architecture.
2Reliability
If shift-based data augmentation is used to train neural networks, then shift-invariance improves, but training time and computing resources increase significantly
Solution Approach 1:
The neural network performs its own shift compensation during inference through the post-pooling adjustment layer, which uses shift parameters detected from the pooled features to correct outputs. This self-service mechanism eliminates the need for extensive shift-based data augmentation during training, as the network learns to detect and compensate for shifts internally rather than requiring all possible shifts to be represented in training data.
Solution Approach 2:
The system changes the parameter representation by introducing shift parameters that are detected from pooled features and used to adjust outputs. Instead of training on multiple shifted versions of each image (parameter expansion through data augmentation), the system uses a compact parameter representation (shift amounts) that is learned and applied during inference, significantly reducing training requirements.
3Reliability
If conventional pooling layers are replaced with specialized pooling layers to achieve shift-invariance, then accuracy improves, but flexibility and adaptability to different neural network applications decrease
Solution Approach 1:
The pooling system becomes dynamic through the post-pooling adjustment layer, which adaptively applies shift compensation based on detected shift parameters. Rather than using a static specialized pooling layer that is rigid and application-specific, the dynamic adjustment mechanism can be applied to any conventional pooling layer, making the system flexible and adaptable to different neural network architectures and applications.
Solution Approach 2:
The post-pooling adjustment mechanism serves as a universal solution that can be applied to any conventional pooling layer across different neural network applications. Rather than requiring application-specific specialized pooling layers, this universal adjustment mechanism maintains compatibility with standard pooling operations while adding shift-invariance capability across diverse applications.
4Measurement precision
If extensive data augmentation is performed to train neural networks with shifted variations, then classification accuracy for shifted inputs improves, but computational resources and processing requirements increase
Solution Approach 1:
The network performs self-correction of shifted inputs through the post-pooling adjustment layer, which detects shift parameters and applies compensatory transformations. This self-service capability eliminates the need to train on extensive shifted variations of training data, significantly reducing computational resources required for data augmentation while maintaining high classification accuracy for shifted inputs.
Solution Approach 2:
The system uses parameter changes (shift parameters detected from pooled features) to correct classification outputs for shifted inputs rather than relying on extensive training data. This parameter-based correction approach requires minimal additional computational resources compared to generating and training on大量 shifted training samples.
Data Source
AI summary
The present disclosure relates to systems, methods, and non-transitory computer readable media for generating shift-resilient neural network outputs based on utilizing a dense pooling layer, a low-pass filter layer, and a downsampling layer of a neural network. For example, the disclosed systems can generate a pooled feature map utilizing a dense pooling layer to densely pool feature values extracted from an input. The disclosed systems can further apply a low-pass filter to the pooled feature map to generate a shift-adaptive feature map. In addition, the disclosed systems can downsample the shift-adaptive feature map utilizing a downsampling layer. Based on the downsampled, shift-adaptive feature map, the disclosed systems can generate shift-resilient neural network outputs such as digital image classifications.