Neural Network Normalization Layer with Learnable Aggregation
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Solution Overview
Problem
Deep neural networks face challenges such as overfitting and the vanishing gradient problem during training, and existing normalization techniques struggle to adaptively handle the relationship between target tasks and style information in images, leading to suboptimal performance.
Innovation Solution
A method and apparatus for training neural networks that employ a combination of batch and instance normalization techniques, using learnable parameters to aggregate and transform output data from previous layers, allowing for intelligent normalization based on the specific requirements of the target task and style information, thereby improving network performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single normalization technique is used, then the training process is simple, but the performance is suboptimal due to inability to adapt to different target tasks and style information
Solution Approach 1:
The patent combines batch normalization and instance normalization into a unified normalization layer that processes feature maps through both techniques simultaneously. The batch normalization component normalizes based on batch-level statistics while the instance normalization component normalizes based on individual feature map statistics, creating a composite normalization approach that leverages the strengths of both methods to improve both performance and adaptability.
Solution Approach 2:
The normalization layer is designed to perform multiple normalization functions within a single architectural component. It can operate in different modes (batch normalization mode, instance normalization mode, or combined mode) depending on the target task requirements, making it universally applicable to various computer vision tasks such as classification, segmentation, and style transfer while adapting to different style information characteristics.
2Adaptability or versatility
If multiple normalization techniques are combined, then adaptability to target tasks improves, but device complexity increases
Solution Approach 1:
The normalization layer applies different normalization strategies to different components of the feature maps locally. Batch normalization is applied to normalize across the batch dimension for tasks requiring style information, while instance normalization is applied to normalize across channels for tasks requiring content preservation. This localized application of different normalization qualities allows the system to adapt to specific task requirements without uniformly increasing complexity across all operations.
Solution Approach 2:
The normalization layer incorporates dynamic switching capability that allows it to adapt its behavior based on the target task and style information characteristics. The layer can dynamically adjust the contribution of batch normalization versus instance normalization components, enabling it to optimize performance for different tasks (e.g., classification vs. style transfer) without requiring separate fixed architectures for each task type.
3Productivity
If normalization is performed without considering style information, then processing is faster, but recognition performance deteriorates
Solution Approach 1:
The normalization layer changes its operational parameters based on the characteristics of style information and target task requirements. When style information is important (e.g., in style transfer tasks), the layer increases the weight of instance normalization to preserve style characteristics while maintaining normalization benefits. When style information is less critical (e.g., in classification tasks), the layer can rely more on batch normalization for faster processing, thus dynamically adjusting parameters to balance speed and performance.
Data Source
AI summary
There is provided is a method and an apparatus for training a neural network capable of improving the performance of the neural network by performing intelligent normalization according to a target task of the neural network. The method according to some embodiments of the present disclosure includes transforming the output data into first normalized data using a first normalization technique, transforming the output data into second normalized data using a second normalization technique and generating target normalized data by aggregating the first normalized data and the second normalized data based on a learnable parameter. At this time, a rate at which the first normalization data is applied in the target normalization data is adjusted by the learnable parameter so that the intelligent normalization according to the target task can be performed, and the performance of the neural network can be improved.