Neural Network Scale-Shift Layer for Multi-Input Feature Modulation
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Solution Overview
Problem
Existing neural network methods struggle to effectively incorporate information from multiple auxiliary inputs, leading to inadequate feature extraction and modulation, which limits the network's ability to accurately predict outputs, especially in classification and generative tasks.
Innovation Solution
The method involves a neural network that scales and shifts layer inputs based on a plurality of auxiliary inputs, allowing each input to modulate specific parts of the layer input, enabling better learning of semantic features and improved performance through the use of sub-networks to determine scaling and shifting values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple auxiliary inputs are naively concatenated and used with a standard scale-and-shift layer, then the neural network can incorporate information from multiple sources, but the downstream layers cannot disentangle the most relevant conditioning information
Solution Approach 1:
The patent divides the layer input into multiple subsets corresponding to different auxiliary inputs. Each subset is processed independently with its own scale-and-shift operation, allowing downstream layers to disentangle relevant information from each auxiliary input source rather than receiving a mixed concatenated signal.
Solution Approach 2:
Different scale-and-shift operations are applied to different subsets of the layer input based on specific auxiliary inputs. This allows each auxiliary input to modulate specific features locally rather than uniformly affecting all features, improving the precision of feature extraction for classification tasks.
2Productivity
If a single auxiliary input is used for scaling and shifting, then the neural network can perform basic feature modulation, but it cannot effectively incorporate information from multiple sources of information
Solution Approach 1:
The scale-and-shift layer is extended to handle multiple auxiliary inputs simultaneously, making it a multi-functional component that can process diverse conditioning information from different sources while maintaining the efficient training benefits of scale-and-shift operations.
Solution Approach 2:
The patent extends the conditioning dimension by accepting multiple auxiliary inputs instead of a single auxiliary input. This adds another dimension to the feature modulation process, allowing the network to incorporate information from multiple sources while maintaining training efficiency.
3Measurement precision
If standard scale-and-shift operations are used without multiple auxiliary inputs, then the training process is simple and fast, but the neural network cannot accurately predict outputs for complex classification tasks
Solution Approach 1:
The layer input is segmented into multiple subsets, each processed by dedicated scale-and-shift operations conditioned on specific auxiliary inputs. This segmentation improves output prediction accuracy for complex tasks while keeping the structural complexity manageable through systematic organization.
Solution Approach 2:
The scale-and-shift operations become dynamic by accepting multiple auxiliary inputs that can change during training and inference. This allows the layer to adapt its feature modulation behavior based on the specific conditioning information available, improving prediction accuracy for varying task requirements.
Data Source
AI summary
Computer-implemented method for determining an output signal based on an input signal and by means of a neural network. The neural network determines the output signal based on a layer output determined by a first layer of the neural network. The layer output is determined based on scaling a layer input of the first layer and shifting the scaled layer input, wherein the scaling and shifting is based on a plurality of auxiliary inputs provided to the first layer.