Mixture-of-Experts Transfer Learning for Diverse Vision Tasks
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Solution Overview
Problem
Existing transfer learning approaches are limited in scalability and task diversity, often requiring expensive re-training on source datasets for every target task and are tested only on a limited suite of natural single-object classification tasks.
Innovation Solution
A scalable strategy for transfer learning using a mixture of experts (MoE) that includes pre-training a diverse set of experts on label hierarchies in large-scale datasets, employing lightweight model selection techniques, and combining experts into a single model via an adapter-based architecture, allowing for efficient fine-tuning on downstream tasks without revisiting the source dataset.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If transfer learning is performed using traditional approaches, then per-task data requirements are reduced, but computational cost increases due to expensive re-training on source datasets for every target task
Solution Approach 1:
The system performs preliminary actions by pre-training multiple expert models on different source tasks before the actual transfer learning task. These expert models are trained in advance on diverse source datasets, creating a repository of specialized models that can be selected and fine-tuned for target tasks without requiring expensive re-training from scratch.
Solution Approach 2:
The system changes parameters by evaluating different expert models based on performance metrics and selecting the most suitable expert for each target task. This involves changing the model selection parameters dynamically based on task characteristics, and adjusting fine-tuning parameters to optimize performance while reducing computational cost.
2Measurement precision
If multiple expert models are evaluated and selected based on performance metrics, then transfer learning accuracy improves, but computational resources increase during the selection process
Solution Approach 1:
The system applies partial action by evaluating only the necessary performance metrics for model selection rather than performing complete re-training and evaluation of all expert models. This selective evaluation approach achieves sufficient accuracy improvement while limiting the computational resources consumed during the selection process.
3Reliability
If expert models are fine-tuned using downstream training data, then task-specific performance improves, but training time increases
Solution Approach 1:
The fine-tuning process benefits from preliminary actions where expert models have already been pre-trained on relevant source tasks. This preliminary pre-training provides a strong foundation that reduces the amount of fine-tuning needed on downstream data, thereby improving task-specific performance while minimizing additional training time.
4Speed
If traditional transfer learning is used, then convergence rates improve, but scalability to diverse vision tasks is limited
Solution Approach 1:
The system segments the transfer learning process into distinct phases: pre-training of multiple expert models on different source tasks, evaluation and selection of appropriate experts, and fine-tuning on target tasks. This segmentation allows the system to maintain fast convergence rates while scaling to diverse vision tasks by selecting and combining appropriate expert models for each specific task.
Solution Approach 2:
The system achieves universality by creating a multi-functional framework where a single transfer learning system can handle diverse vision tasks through multiple expert models. Each expert model is trained on different source tasks, and the system can universally apply the appropriate expert to any target task, thereby scaling across diverse domains while maintaining efficient convergence.
Data Source
AI summary
Generally, the present disclosure is directed to systems and methods that provide a simple, scalable, yet effective strategy to perform transfer learning with a mixture of experts (MoE). In particular, the transfer of pre-trained representations can improve sample efficiency and reduce computational requirements for new tasks. However, representations used for transfer are usually generic, and are not tailored to a particular distribution of downstream tasks. In contrast, example systems and methods of the present disclosure use expert representations for transfer with a simple, yet effective, strategy.