Multimodal Model Handling Missing Sensor Data via Fixed-Dimensional Templates
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Solution Overview
Problem
Current multimodal machine learning models require all modalities to be present during training and inference, making them unsuitable for real-world applications where sensor data may be missing due to outages or environmental interference, and are costly to implement and maintain.
Innovation Solution
An attention-based neural network architecture that processes multimodal data with missing modalities by generating a fixed-dimensional vector representation, allowing the model to handle incomplete data during both training and inference, using transformer layers and a Conditional Variational AutoEncoder (CVAE) framework.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple-redundant sensors are used to ensure all modalities are available, then reliability of data availability is improved, but device complexity and cost increase
Solution Approach 1:
The patent segments the training process into two distinct phases: a first training phase using only modal-complete data, and a second training phase using modal-incomplete data. This segmentation allows the model to first learn from high-quality complete data, then adapt to handle missing modalities without requiring redundant sensors.
Solution Approach 2:
The patent changes the parameter of data completeness by introducing a second training phase that specifically processes modal-incomplete data. This parameter change enables the model to learn robust representations that tolerate missing modalities, eliminating the need for redundant sensor systems.
2Manufacturing precision
If training data is cleaned to ensure all modalities are present, then manufacturing precision of training data is improved, but productivity of dataset building decreases
Solution Approach 1:
The patent introduces dynamic training stages that adaptively process different types of training data. The first stage processes modal-complete data for foundational learning, while the second stage processes modal-incomplete data for robustness training. This dynamic approach eliminates the need for time-consuming data cleaning while maintaining training effectiveness.
Solution Approach 2:
The patent performs preliminary processing by creating a fixed-dimensional vector representation template during the first training stage. This template serves as a foundation that enables the model to subsequently handle incomplete data without requiring extensive cleaning or preprocessing of the incomplete samples.
3Ease of operation
If existing multimodal models are used with missing modalities, then ease of operation is maintained, but measurement precision of predictions deteriorates
Solution Approach 1:
The patent ensures continuous useful action by implementing a two-stage training process that continuously improves model capabilities. The first stage establishes baseline performance on complete data, while the second stage continuously refines the model's ability to handle incomplete data, maintaining prediction accuracy without requiring complex operational changes.
Solution Approach 2:
The patent enables the model to self-adapt to missing modalities through the second training stage, where it learns to compensate for absent modalities using available data. This self-service capability allows the model to maintain high prediction accuracy automatically, without requiring external intervention or complex operational adjustments.
Data Source
AI summary
Multimodal training data comprising samples of a prediction target is received. Each sample includes at least a subset of the full set of a plurality of modalities, and the samples collectively include instances of each modality. An attention-based encoder receives sets of training vectors for the samples in fixed-dimensional input vector format, and generates a fixed-dimensional vector representation template for the prediction target. The number of dimensions in the template is constant and is independent of the number of modalities represented by the training vectors. The attention-based encoder uses the samples and the fixed-dimensional vector representation template to generate, from the training vectors for the samples, a latent distribution. The samples in fixed-dimensional input vector format and the latent distribution are used as input to a second attention-based neural network to generate an attention-based decoder that can predict from samples with missing modalities.