Transfer Learning Model Selection via Embedding Similarity
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Solution Overview
Problem
The selection of a suitable source model for transfer learning in deep learning scenarios is challenging due to sparse training data, leading to sub-optimal performance and computationally expensive operations, especially when fine-tuning multiple source models for natural language processing tasks.
Innovation Solution
A system and method that utilizes an AI platform with a machine learning model manager, metric manager, and training manager to select a base machine learning model for transfer learning by calculating similarity metrics between test and generated labels, and replacing the classification layer with a new classifier mapped to the target dataset, allowing for efficient training on target data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple source models are fine-tuned for transfer learning, then model performance may improve, but computational cost and time consumption increase significantly
Solution Approach 1:
The patent applies preliminary action by pre-calculating similarity metrics between source models and target domains before the actual transfer learning process. The system computes cosine similarity between source model embeddings and target domain embeddings in advance, allowing the selection of the most suitable source model without having to fine-tune multiple models. This preliminary assessment eliminates unnecessary computational waste on unsuitable source models.
Solution Approach 2:
The system employs self-service through automated similarity-based selection of source models. The transfer learning system automatically identifies the most suitable source model by comparing embedding similarities, eliminating the need for manual evaluation or trial-and-error fine-tuning of multiple models. The system serves itself by making intelligent selections based on pre-computed metrics.
2Reliability
If multiple source models are fine-tuned for transfer learning, then model performance may improve, but computational resources and costs increase
Solution Approach 1:
The patent applies preliminary action by pre-calculating similarity metrics between source models and target domains before the actual transfer learning process. The system computes cosine similarity between source model embeddings and target domain embeddings in advance, allowing the selection of the most suitable source model without having to fine-tune multiple models. This preliminary assessment eliminates unnecessary computational waste on unsuitable source models.
3Adaptability or versatility
If a suitable source model is selected using traditional methods, then transfer learning can proceed, but the selection process becomes complex and time-consuming
Solution Approach 1:
The patent replaces the mechanical trial-and-error selection process with an automated embedding-based similarity computation system. Instead of manually evaluating multiple source models through complex fine-tuning procedures, the system uses neural network embeddings to automatically compute similarity metrics and identify the most suitable source model programmatically.
Solution Approach 2:
The patent changes the selection parameter from complex performance metrics requiring full fine-tuning to simple embedding similarity scores. By representing source models and target domains as vectors in embedding space, the system can compute suitability using straightforward cosine similarity calculations, dramatically simplifying the selection process while maintaining accuracy.
Data Source
AI summary
A computer system, product, and method are provided. The computer system includes an artificial intelligence (AI) platform operatively coupled to a processor. The AI platform includes tools in the form of a machine learning model (MLM) manager, a metric manager, and a training manager. The MLM manager accesses a plurality of pre-trained source MLMs, and inputs a plurality of data objects of a test dataset into each of the source MLMs. The test dataset includes the plurality of data objects associated with respective labels. For each source MLM, associated labels are generated from the inputted data objects and a similarity metric is calculated. The MLM manager selects a base MLM to be used for transfer learning from the plurality of source MLMs based upon the calculated similarity metric. The training manager trains the selected base MLM with a target dataset for the target domain.