Dynamic Soft Normalization for Neural Network Data Adaptation
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Solution Overview
Problem
Existing normalization techniques in neural networks are limited by fixed internal parameters that do not adjust based on the specific features of the input data, leading to suboptimal performance across different tasks and input samples.
Innovation Solution
A dynamic soft normalization (DSN) process that combines multiple normalization techniques, such as batch normalization, instance normalization, and layer normalization, with a soft weighting engine to dynamically adjust the normalization process based on the input data, allowing for sample-aware adaptation and improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If fixed internal parameters are used in normalization techniques, then device complexity is reduced, but adaptability to different input data features deteriorates
Solution Approach 1:
The patent implements dynamic normalization by replacing fixed internal parameters with learnable parameters that are updated during training. The normalization layer now adapts its parameters (gamma and beta) based on the specific features of input data, transforming a static normalization process into a dynamic one that responds to different data characteristics.
Solution Approach 2:
The invention changes the parameters of the normalization technique from fixed values to learnable parameters. During training, the system learns optimal parameter values (gamma and beta) for different normalization techniques, and during inference, it dynamically selects and applies the appropriate parameters based on input data features, thereby improving adaptability without excessive complexity.
2Measurement precision
If multiple normalization techniques are combined dynamically, then accuracy is improved, but device complexity increases
Solution Approach 1:
The system dynamically combines multiple normalization techniques by maintaining a set of learnable parameters for each technique (BN, IN, LN, GN). During inference, it selects and applies the appropriate normalization technique and parameters based on the specific features of the input data, achieving high accuracy through adaptive selection rather than static application.
Solution Approach 2:
The normalization layer is designed to support multiple normalization techniques (batch normalization, instance normalization, layer normalization, group normalization) within a single unified structure. This multi-functional design allows the system to adapt to different input data characteristics using the most suitable technique, improving accuracy while managing complexity through a cohesive architecture.
3Adaptability or versatility
If normalization parameters are learned during training, then adaptability to different tasks is improved, but loss of time during training increases
Solution Approach 1:
The system learns task-specific parameters (gamma and beta) for each normalization technique during training. By maintaining learnable parameters for multiple techniques and updating them during training based on task requirements, the system achieves high task adaptability. The training process optimizes these parameters to capture task-specific characteristics, enabling flexible deployment across different tasks.
Data Source
AI summary
Methods, apparatus, systems, and articles of manufacture to dynamically normalize data in neural networks are disclosed. An apparatus for use with a machine learning model includes at least one normalization calculator to generate a plurality of alternate normalized outputs associated with input data for the machine learning model. Different ones of the alternate normalized outputs based on different normalization techniques. The apparatus further includes a soft weighting engine to generate a plurality of soft weights based on the input data. The apparatus also includes a normalized output generator to generate a final normalized output based on the plurality of alternate normalized outputs and the plurality of soft weights.