Contextual Convolution Blocks for Spatial Feature Detection
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Solution Overview
Problem
Conventional neural networks face challenges in effectively incorporating spatial information while maintaining translation invariance, leading to inefficiencies in parameter usage and computational resources during training and inference.
Innovation Solution
The implementation of a convolutional neural network with contextual convolution blocks that generate spatial weight masks using spatially sensitive mask functions, allowing for translation-invariant feature detection and reducing the need for fully connected layers, thereby improving expressive power and computational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional neural networks use fully connected layers to incorporate spatial information, then the network can process spatial data, but the parameter usage becomes inefficient and computational resources are wasted
Solution Approach 1:
The patent applies local quality by making the convolutional layer parameters (kernels) spatially localized and translation-invariant, where each kernel operates on local receptive fields rather than globally connected parameters. This allows the network to process spatial information efficiently by applying the same learned filters across different spatial locations, reducing parameter count while maintaining spatial processing capability.
Solution Approach 2:
The patent implements universality through shared convolutional kernels that perform the same feature detection function across all spatial locations in the input. The same set of learnable parameters (kernels) is universally applied to detect features regardless of their position in the spatial domain, eliminating the need for separate parameters for each spatial location as in fully connected layers.
2Reliability
If conventional neural networks maintain translation invariance, then the network achieves robustness to spatial transformations, but the ability to detect spatially specialized features is reduced
Solution Approach 1:
The patent applies dynamics by introducing dynamically generated spatial weight masks that modulate the convolutional layer outputs based on spatial position. These masks are generated through additional neural network layers that process the convolutional features and produce position-dependent weighting factors, allowing the network to adaptively emphasize or suppress features at different spatial locations while maintaining the underlying translation-invariant convolutional operations.
3Device complexity
If standard convolutional layers are used, then the network structure is simple, but computational resources and training time are excessive
Solution Approach 1:
The patent applies preliminary action by pre-processing the convolutional layer outputs through contextual processing that generates spatial weight masks before the final feature extraction. This preliminary contextual analysis allows the network to identify and prioritize important spatial regions and features early in the processing pipeline, enabling more efficient subsequent computations and faster convergence during training by focusing computational resources on relevant features.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing an input through each of a plurality of layers of a neural network to generate an output, wherein the plurality of layers comprise a convolutional layer. One of the methods includes: receiving a layer input for the convolutional layer; processing the layer input to generate a layer output for the convolutional layer, comprising determining a convolution between the layer input and a filter associated with the convolutional layer; generating a spatial weight mask for the convolutional layer by using a contextual convolution block in accordance with a set of one or more spatially sensitive mask functions defined in the contextual convolution block; and determining a weighted layer output for the convolutional layer, comprising determining a product between the spatial weight mask and the layer output of the convolutional layer.