Neural Network Attention Map Noise Suppression via Nonlinear Transformation
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Solution Overview
Problem
Existing neural networks that apply attention mechanisms directly to data often emphasize unnecessary features due to noise components, leading to reduced accuracy and increased computational complexity.
Innovation Solution
The proposed solution involves generating an attention map from input data after performing a nonlinear transformation, which helps in separating and removing noise components, thereby improving processing accuracy while reducing the number of learning parameters and computational load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If attention is directly applied to input data, then the attention mechanism can process the data efficiently, but unnecessary features are emphasized leading to reduced accuracy
Solution Approach 1:
The patent applies preliminary action by performing nonlinear transformation on the input data before applying the attention mechanism. This preliminary transformation processes the data to remove noise components and unnecessary features, so that when attention is subsequently applied, it operates on cleaned data rather than raw data containing irrelevant information.
Solution Approach 2:
The patent extracts the harmful noise components and unnecessary features from the input data through the nonlinear transformation step. By separating and removing these unwanted elements before the attention mechanism processes the data, the system prevents unnecessary features from being emphasized while maintaining the efficiency of the attention mechanism.
2Productivity
If attention is applied to data with noise components, then the attention mechanism processes all features, but computational complexity increases
Solution Approach 1:
The patent extracts and removes noise components from the input data through nonlinear transformation before applying attention. This reduces the amount of meaningful information the attention mechanism must process, thereby reducing computational complexity while maintaining processing throughput.
Solution Approach 2:
By performing data cleaning and transformation as a preliminary step before attention processing, the system prepares the data in advance to reduce the computational burden during the actual attention mechanism execution, optimizing the balance between processing throughput and computational complexity.
Data Source
AI summary
An information processing apparatus performing inference or learning using a neural network generates an attention map from input data, performs a nonlinear transformation on the input data, obtains, based on the generated attention map and an output obtained based on the nonlinear transformation on the input data, a feature amount map having a channel dimension for storing an element vector and one or more spatial dimensions, and performs an inference or learning process based on the obtained feature amount map.