Transfer Learning Weight Policy for Small Target Datasets
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Solution Overview
Problem
Conventional transfer learning techniques struggle to effectively utilize large-scale source datasets to improve performance on small-scale target datasets, as they rely on fixed weight assignments that do not adaptively optimize for the target domain, leading to suboptimal performance.
Innovation Solution
The Learning to Transfer Learn (L2TL) architecture adaptively learns weight assignments for a deep learning model by employing two phases of optimization: gradient descent-based learning for encoder and classifier weights, followed by reinforcement learning to maximize an evaluation metric on a target evaluation dataset, using a policy model to dynamically select relevant samples.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If fixed weight assignments are used in conventional transfer learning, then the model structure remains simple and easy to implement, but the model performance on target dataset is suboptimal because weights do not adaptively optimize for the target domain
Solution Approach 1:
The patent transforms the static fixed weight assignment into a dynamic adaptive weight assignment mechanism. The weight assignment function is learned through reinforcement learning, allowing the model to dynamically adjust weights based on the target domain characteristics. This enables the system to adaptively optimize for the target dataset while maintaining a relatively simple architectural structure.
Solution Approach 2:
The patent introduces a policy model as an intermediary component that learns the optimal weight assignment function. This policy model acts as a mediator between the source and target datasets, determining how much weight to assign to source data based on the current target domain context. This intermediary enables adaptive optimization without requiring complex modifications to the entire model architecture.
2Reliability
If reinforcement learning is used to learn weight assignments, then the model can adaptively optimize for the target domain, but the training time and computational resources increase significantly
Solution Approach 1:
The patent segments the training process into distinct phases: initial training using source data with fixed weights, then switching to reinforcement learning-based adaptive weight assignment for target domain optimization. This segmentation allows the model to benefit from the efficiency of conventional training initially, then transition to adaptive optimization only when needed, reducing overall training time compared to applying reinforcement learning throughout the entire training process.
Solution Approach 2:
The patent performs preliminary training on source data using conventional methods before applying reinforcement learning for adaptive weight assignment. This preliminary action pre-trains the model with abundant source data, establishing a good initial state that reduces the computational burden and training time required for subsequent adaptive optimization on the target domain.
3Quantity of substance
If all source data samples are used for training, then the model can leverage maximum information from source dataset, but the model may be distracted by irrelevant samples that do not generalize well to target domain
Solution Approach 1:
The patent applies local quality by assigning different weights to different source data samples based on their relevance to the target domain. The learned weight assignment function identifies and emphasizes locally relevant samples while suppressing irrelevant ones. This allows the model to effectively utilize the maximum relevant information from the source dataset without being distracted by irrelevant samples, improving generalization to the target domain.
Data Source
AI summary
A method includes receiving a source data set and a target data set and identifying a loss function for a deep learning model based on the source data set and the target data set. The loss function includes encoder weights, source classifier layer weights, target classifier layer weights, coefficients, and a policy weight. During a first phase of each of a plurality of learning iterations for a learning to transfer learn (L2TL) architecture, the method also includes: applying gradient decent-based optimization to learn the encoder weights, the source classifier layer weights, and the target classifier weights that minimize the loss function; and determining the coefficients by sampling actions of a policy model. During a second phase of each of the plurality of learning iterations, determining the policy weight that maximizes an evaluation metric.