Black-Box Transfer Learning via Input Transformation
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Solution Overview
Problem
Current transfer learning techniques require full access to the source model architecture and weights, making them impractical for access-limited machine learning systems, such as black-box models, where only input-output responses are observable, and data scarcity and resource constraints are common, hindering the application of transfer learning in scenarios like medical imaging.
Innovation Solution
The system employs black-box adversarial reprogramming using zeroth-order optimization and multi-label mapping techniques to reprogram access-limited ML models without modifying their architecture or weights, leveraging input-output responses to adapt pre-trained models for new tasks, even with scarce data and limited resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current transfer learning techniques are used, then model performance can be improved through knowledge transfer, but full access to source model architecture and weights is required which is not available in black-box settings
Solution Approach 1:
The patent introduces an intermediary transformation function that maps target domain inputs to source domain inputs. This mediator enables transfer learning without direct access to the source model architecture, allowing the system to leverage pre-trained models in black-box settings by transforming inputs rather than modifying model weights.
Solution Approach 2:
The patent creates a virtual copy of the source model's behavior through the transformation function. Instead of accessing the actual source model weights and architecture, the system learns a transformation that replicates the source model's predictive capability on target domain data, effectively copying its functionality without direct access.
2Productivity
If gradient-based learning approaches are used, then efficient optimization is achieved, but they become infeasible in access-limited black-box settings where gradients cannot be obtained
Solution Approach 1:
The patent replaces gradient-based optimization mechanisms with a transformation function approach. Instead of using gradients to update model weights directly, the system learns an input transformation function that can be optimized through input-output pairs, substituting the gradient-based mechanical process with a transformation-based approach suitable for black-box models.
3Quantity of substance
If pre-trained models are used in black-box settings, then data scarcity is addressed by leveraging existing models, but the models cannot be fine-tuned or adapted without full access
Solution Approach 1:
The patent introduces dynamic adaptability through the transformation function that can be learned and optimized for different target domains. The transformation function adapts to new tasks and domains by learning from target domain input-output pairs, enabling the system to dynamically adjust to different应用场景 without requiring fine-tuning of the original pre-trained model.
Data Source
AI summary
Transfer learning in machine learning can include receiving a machine learning model. Target domain training data for reprogramming the machine learning model using transfer learning can be received. The target domain training data can be transformed by performing a transformation function on the target domain training data. Output labels of the machine learning model can be mapped to target labels associated with the target domain training data. The transformation function can be trained by optimizing a parameter of the transformation function. The machine learning model can be reprogrammed based on input data transformed by the transformation function and a mapping of the output labels to target labels.