Multimodal Encoder Adapter for Efficient Fine-Tuning
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Solution Overview
Problem
Conventional machine learning systems face challenges in efficiently performing multimodal tasks due to the need for re-training, which is time-consuming and costly, and are often trained on noisy data that can lead to incorrect outputs.
Innovation Solution
A machine learning system that uses a multimodal encoder to generate multiple outputs at different scales, aggregates these outputs using a multi-scale aggregator, and employs an adapter for efficient fine-tuning, allowing it to increase performance without re-training all parameters of the pre-trained encoder.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a conventional machine learning system is re-trained to perform multimodal tasks, then the performance may be improved, but the process is expensive and time-consuming
Solution Approach 1:
The patent segments the model into a pre-trained multimodal encoder and a task-specific adapter. The encoder handles general multimodal understanding while the adapter handles task-specific fine-tuning. This segmentation allows efficient fine-tuning without re-training the entire model, resolving the contradiction between improving performance and reducing training time.
Solution Approach 2:
The patent extracts the pre-trained multimodal encoder from the complete system and uses it as a foundation. By taking out the pre-trained components, the system can focus computational resources on training only the adapter layers, significantly reducing training time while maintaining performance improvements.
2Ease of manufacture
If a conventional machine learning system is trained on existing datasets, then training is simpler, but the data may be noisy which negatively impacts performance
Solution Approach 1:
The adapter acts as an intermediary layer between the pre-trained encoder and the task-specific requirements. It mediates the influence of noisy training data by learning to filter and select relevant information, allowing the system to maintain performance even when training on noisy existing datasets without requiring complex data cleaning.
Solution Approach 2:
The patent changes the parameters of the adapter layers to adapt to specific task requirements while using the pre-trained encoder's parameters as initialization. This parameter change approach allows the system to leverage pre-trained knowledge while adapting to noisy training data, improving performance without requiring clean data from scratch.
3Measurement precision
If the capacity of the multimodal encoder is increased to handle complex tasks, then the accuracy of outputs improves, but the system complexity increases
Solution Approach 1:
The patent segments the capacity enhancement into two parts: the pre-trained encoder maintains its original architecture while the adapter adds task-specific capacity. This segmentation allows increasing output accuracy for complex tasks without proportionally increasing overall system complexity, as the bulk of the computational load remains in the pre-trained encoder.
Solution Approach 2:
The adapter implements partial action by adding only the necessary capacity for the specific task at hand, rather than excessively increasing the entire encoder's capacity. This partial approach achieves sufficient accuracy for complex tasks while minimizing the increase in system complexity.
Data Source
AI summary
Systems and methods for multimodal machine learning are provided. According to one aspect, a method for multimodal machine learning includes obtaining a prompt; encoding the prompt using a multimodal encoder to obtain a prompt embedding, wherein the encoding comprises generating a plurality of multi-head attention (MHA) outputs corresponding to a plurality of different scales, respectively, and combining the plurality of MHA outputs using a multi-scale aggregator; and generating a response to the prompt based on the prompt embedding.