Joint Representation Learning via Mutual Information
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Solution Overview
Problem
Current visual representation learning methods face challenges in achieving semantically meaningful representations with minimal supervision, as supervised learning requires large annotated datasets and unsupervised learning may not produce semantically meaningful results.
Innovation Solution
A joint representation learning system that utilizes mutual information to train image and text models together, employing a critic function to maximize the compatibility between image and text embeddings, allowing for unsupervised learning with semantically meaningful representations using image/text pairs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If supervised learning is used to achieve semantically meaningful representations, then the semantic quality of representations is improved, but the requirement for large annotated datasets increases
Solution Approach 1:
The patent introduces text as an intermediary modality that bridges the gap between unsupervised learning and semantic quality. By computing mutual information between image embeddings and text embeddings, the system transfers semantic knowledge from text (which has inherent semantic structure) to images without requiring annotated images. The text acts as a mediator that provides semantic guidance to the unsupervised image representation learning process.
2Ease of operation
If unsupervised learning is used to minimize supervision requirements, then the data annotation requirement is reduced, but the semantic meaningfulness of representations deteriorates
Solution Approach 1:
Text serves as an intermediary that carries semantic information from the supervised domain to the unsupervised image learning process. The mutual information computation between image and text embeddings allows semantic knowledge to be transferred without direct annotation of images, maintaining semantic quality while minimizing supervision requirements.
Solution Approach 2:
The patent changes the learning objective parameter from traditional unsupervised criteria (e.g., reconstruction error) to mutual information maximization between image and text embeddings. This parameter change enables the unsupervised learning process to capture semantic relationships by focusing on the information shared between modalities rather than just data fidelity.
3Measurement precision
If mutual information maximization is employed to learn joint representations, then the semantic quality with minimal supervision is improved, but the computational complexity increases
Solution Approach 1:
The patent extracts and focuses only on the essential information shared between image and text modalities through mutual information computation. By computing MI between embedding spaces rather than raw data, the system extracts only the relevant semantic information while discarding redundant details, reducing computational complexity compared to processing full-resolution images and text.
Data Source
AI summary
The disclosure provides a framework or system for learning visual representation using a large set of image/text pairs. The disclosure provides, for example, a method of visual representation learning, a joint representation learning system, and an artificial intelligence (AI) system that employs one or more of the trained models from the method or system. The AI system can be used, for example, in autonomous or semi-autonomous vehicles. In one example, the method of visual representation learning includes: (1) receiving a set of image embeddings from an image representation model and a set of text embeddings from a text representation model, and (2) training, employing mutual information, a critic function by learning relationships between the set of image embeddings and the set of text embeddings.