Multimodal Unsupervised Meta-Learning Encoder for Cross-Modal Task Adaptation
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Solution Overview
Problem
Current unsupervised meta-learning technologies are limited to single-modal data, making it difficult to implement artificial intelligence that can handle multimodal data like humans do, which is essential for flexible thinking and accurate task performance in real-world applications.
Innovation Solution
A multimodal unsupervised meta-learning apparatus that extracts conceptual features from a massive multimodal dataset without labels, generates a source task similar to a target task, and derives a learning method to improve learning efficiency and performance, using a combination of neural networks and feature extraction techniques to train models with a small number of target datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If unsupervised meta-learning is applied to single-modal data, then learning efficiency is improved, but the ability to handle multimodal data like humans is lost
Solution Approach 1:
The patent applies universality by designing a unified multimodal unsupervised meta-learning framework that can handle multiple data modalities (image, audio, text) simultaneously. The system uses a single encoder that processes different modalities through their respective embedding layers, enabling one model to perform multiple tasks across different data types without requiring separate single-modal models for each modality.
Solution Approach 2:
The patent merges multiple single-modal processing streams into a unified multimodal framework. Different modalities are processed through separate embedding layers (image embedding layer, audio embedding layer, text embedding layer) and then combined through a shared encoder structure, allowing the system to integrate information from multiple sources while maintaining the efficiency of unsupervised learning.
2Measurement precision
If massive labeled data is used for training, then model accuracy is improved, but data acquisition cost and time increase significantly
Solution Approach 1:
The patent extracts labels from the data through unsupervised learning techniques. Instead of requiring pre-labeled data, the system automatically discovers patterns and structures in the data by training the encoder to reconstruct or classify data representations, thereby extracting meaningful labels and concepts without human annotation.
Solution Approach 2:
The system performs self-labeling through unsupervised meta-learning. The encoder learns to represent data in a way that automatically organizes it into meaningful categories and concepts, allowing the model to serve its own labeling needs without external intervention or pre-labeled data.
3Loss of information
If multimodal data is processed using traditional deep learning, then comprehensive information capture is improved, but computational resource requirements increase
Solution Approach 1:
The patent segments the processing of different modalities into separate embedding layers, allowing each modality to be processed independently through its own specialized layer. This segmentation enables efficient processing by avoiding the computational burden of processing all modalities simultaneously through a single monolithic model, while still capturing comprehensive information through the integrated encoder.
Data Source
AI summary
Disclosed herein are a multimodal unsupervised meta-learning method and apparatus. The multimodal unsupervised meta-learning method includes training, by a multimodal unsupervised feature representation learning unit, an encoder configured to extract features of individual single-modal signals from a source multimodal dataset, generating, by a multimodal unsupervised task generation unit, a source task based on the features of individual single-modal signals, deriving, by a multimodal unsupervised learning method derivation unit, a learning method from the source task using the encoder, and training, by a target task performance unit, a model based on the learning method and features extracted from a small number of target datasets by the encoder, thus performing the target task.