High-Dimensional Transfer Learning Across Image And Sensor Channels
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Solution Overview
Problem
Existing machine learning models require separate training for different sensor types, leading to inefficiencies and lack of knowledge transfer between models with varying numbers of channels.
Innovation Solution
Utilize weights from a pre-trained model with a lower number of channels to pre-train and fine-tune a model with a higher number of channels, applying activation functions and normalization to facilitate training and improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If separate models are trained for different sensor types with different numbers of channels, then each model can be optimized for its specific sensor data, but training time and computational resources are wasted due to lack of knowledge transfer
Solution Approach 1:
The patent applies preliminary action by pre-training a base model on source sensor data with fewer channels before adapting it to target sensor data with more channels. The pre-trained model's weights and features serve as a foundation, eliminating the need to train from scratch and significantly reducing training time while maintaining accuracy.
Solution Approach 2:
The patent utilizes parameter changes by modifying the number of input channels to accommodate different sensor types. The model architecture is adapted by adding or removing channels while preserving the core trained parameters and weights, enabling knowledge transfer across sensors with different dimensionalities.
2Adaptability or versatility
If a model is trained from scratch for each sensor type, then the model can be perfectly adapted to that specific sensor, but computational resources and training efficiency are significantly reduced
Solution Approach 1:
The patent implements universality by creating a universal base model that can be adapted to multiple sensor types with different numbers of channels. The core model architecture and pre-trained weights serve multiple purposes across different sensor domains, enabling one model to function effectively for various sensor inputs without requiring separate training for each.
Solution Approach 2:
By performing preliminary pre-training on a base sensor type, the patent establishes a versatile foundation that can be quickly adapted to other sensor types. This preliminary action creates a multi-functional model that maintains high training efficiency while achieving sensor-specific adaptability through subsequent fine-tuning.
3Productivity
If knowledge transfer is attempted between models with different numbers of channels, then training efficiency improves, but model complexity and architectural adaptation requirements increase
Solution Approach 1:
The patent applies segmentation by dividing the model into a stable base architecture and adaptable channel components. The core model structure remains unchanged and reusable, while only the input channel layer requires modification. This segmentation allows knowledge transfer to proceed efficiently with minimal architectural complexity increases.
Solution Approach 2:
The patent manages complexity by making targeted parameter changes only where necessary - specifically in the input channel dimension - while preserving all other model parameters and architectural decisions. This selective parameter modification enables knowledge transfer without requiring comprehensive architectural redesign.
Data Source
AI summary
An example method comprises applying learning (e.g., weights) developed for training a first model for a first data set of image data to training a second model for a second data set of sensor data. The first and the second data sets may be from the same environment. The second data set has a greater number of channels than the first data set. Weights of layers determined in the first model training may be initially applied to training the second model for the second set of data. Channels of the second data set equal to the number of channels of the first data set may be utilized for each of the layers, using the same weights from the first model. All or some of the channels may be applied in training the second model and using the layers, but determining new weights for the generation of the second trained model.