Neural Network Normalization-Activation Layer Architecture Search
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Solution Overview
Problem
Conventional neural network architectures, such as batch norm + ReLu, often face stability issues during training and do not generalize well across different tasks, leading to suboptimal performance in image understanding and generation tasks.
Innovation Solution
A system that determines the architecture for a normalization-activation neural network layer (NA layer) by generating candidate architectures, evaluating their performance across multiple neural network architectures, and selecting the best architecture based on overall fitness, thereby improving training stability and performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional architectures like batch norm + ReLu are used, then the implementation is simple and well-known, but training stability is poor and performance is suboptimal
Solution Approach 1:
The patent changes the parameters of the normalization-activation layer by searching through multiple architectural configurations (different normalization methods, activation functions, and their combinations) to find the optimal parameter set that maximizes training stability and performance on image understanding tasks
Solution Approach 2:
The patent creates multiple candidate architectures by copying and varying the conventional batch norm + ReLu structure, then evaluates each copy to determine which variant performs best, effectively exploring the architecture search space systematically
2Productivity
If conventional architectures are used, then the computational resources required are reduced, but performance on image understanding and generation tasks is suboptimal
Solution Approach 1:
The system automatically searches for optimal architectures by evaluating multiple candidate designs against performance metrics, allowing the architecture search process to self-optimize without requiring manual intervention or extensive computational resources for each individual evaluation
Solution Approach 2:
The patent evaluates multiple candidate architectures (excessive action) but uses rejection criteria to prune poor performers early, thereby reducing the total computational resources needed compared to exhaustively evaluating all possible architectures
3Reliability
If the search space is explored thoroughly to find optimal architecture, then performance is maximized, but computational resources consumed increase
Solution Approach 1:
The patent implements feedback through performance evaluation of candidate architectures, where the results of evaluating one architecture inform the search process for subsequent architectures, allowing the system to converge on optimal solutions more efficiently
Solution Approach 2:
The patent performs preliminary evaluations of candidate architectures using rejection criteria before full-scale training, allowing the system to filter out poor performers early and allocate computational resources more efficiently to promising candidates
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for searching for an architecture for an activation-normalization layer to be included in a neural network to replace a set of layers that receive a layer input comprising a plurality of values, apply one or more normalization operations to the values in the layer input to generate a normalized layer input, and apply an element-wise activation function to the normalized layer input to generate a layer output.