Hypernetwork Weight Sharing and Pruning for Vision-Language Models
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Solution Overview
Problem
Existing weight sharing techniques for vision and language models are limited by manually designed rules that restrict flexibility, leading to performance drops when deploying on resource-constrained devices.
Innovation Solution
Implement cross-modal and block-wise weight sharing, combined with pruning, using a hypernetwork to automatically determine sharing and pruning points, optimizing the model's parameter utilization and reducing size without sacrificing accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If manually designed weight sharing rules are used, then the model structure is simplified, but the flexibility and performance are reduced
Solution Approach 1:
The system uses self-supervised learning where the model automatically learns optimal weight sharing patterns through contrastive learning on positive and negative pairs, eliminating the need for manual rule design while maintaining structural simplicity
Solution Approach 2:
The patent dynamically adjusts weight sharing parameters through learnable transformation matrices and projection layers, allowing the model to adapt weight sharing flexibility based on the specific task and data characteristics rather than using fixed manual rules
2Quantity of substance
If the number of parameters is reduced for deployment on mobile devices, then the model size is decreased, but the prediction quality may drop
Solution Approach 1:
The patent merges vision and language encoders into a unified multimodal representation space through weight sharing and contrastive learning, reducing total parameter count while maintaining predictive capability through shared semantic understanding
Solution Approach 2:
The shared encoder weights serve multiple functions by processing both vision and language inputs, allowing a single set of parameters to handle diverse modalities and tasks, thereby reducing overall parameter requirements while maintaining versatility
3Quantity of substance
If existing weight sharing techniques are used, then parameter reuse is encouraged, but the flexibility of weight sharing is restricted
Solution Approach 1:
The patent implements dynamic weight sharing where the sharing pattern is not fixed but adapts through learnable projection layers and task-specific transformations, allowing the system to optimize parameter reuse while maintaining flexibility for different modalities and tasks
Data Source
AI summary
A method of performing a multimodal tasks by using a multimodal model that includes a text encoder and a vision encoder, may include obtaining a text feature from the query via the text encoder; obtaining an image feature from the one or more input images via the vision encoder; and outputting a response to the query based on similarity between the text feature and the image feature, wherein weights vectors of the text encoder and the vision encoder are pruned and shared according to a sharing vector and a pruning vector that are generated by a hypernetwork, and wherein the hypernetwork and the multimodal model are jointly trained to minimize at least one of a difference between the weight vectors in the text encoder and the vision encoder, a difference between the weight vectors in different layers of the text encoder, and a number of parameters in the multimodal model.