Rank Distillation for Cross-Architecture Score Alignment
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Solution Overview
Problem
Conventional supervised machine learning model training methods are limited by the need to fix a trained model during the training process of a new model, leading to mismatches in score distributions and inhibited performance, especially when the trained model and new model have different architectures or datasets.
Innovation Solution
A method involving rank distillation is employed, where teacher scores are transformed to match the probability distribution of student model scores, using a distillation loss function to update the primary loss function, thereby aligning the student model's performance with the teacher model's behavior without retraining from a primitive state.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the trained model is held fixed during the training process of the new model, then the training process is simplified and computationally efficient, but mismatches in score distributions occur and performance is inhibited
Solution Approach 1:
The patent introduces an intermediary transformation process that maps teacher model scores to match the student model's score distribution. This transformation acts as a mediator between the fixed teacher model and the student model, enabling knowledge transfer without requiring the teacher model to be retrained or modified, thus maintaining training efficiency while improving performance alignment
Solution Approach 2:
The patent transforms the teacher model's score distribution by applying parameter changes to align it with the student model's score distribution. This involves modifying the statistical parameters (mean, variance, etc.) of the teacher scores to match the student model's characteristics, resolving the distribution mismatch while keeping the teacher model fixed
2Adaptability or versatility
If teacher scores from different model architectures are used, then knowledge transfer between diverse models is enabled, but score distribution mismatches inhibit training performance
Solution Approach 1:
The patent applies parameter changes to transform teacher scores from different architectures into the student model's score distribution space. By adjusting statistical parameters and applying distribution matching techniques, the system enables effective knowledge transfer across diverse model architectures while maintaining training performance
Solution Approach 2:
The patent creates a universal score transformation framework that can handle teacher models from various architectures. The transformation process is architecture-agnostic and can adapt to different model types, enabling broad knowledge transfer capability while resolving distribution mismatches through standardized parameter transformation
Data Source
AI summary
Computer-implemented method and systems to improve training and performance of artificial intelligence (AI) systems having one or more machine learning models stored in one or more data storage mediums connected in at least one computing network is provided. The method comprises receiving student model scores, generated by a student machine learning model stored in a data storage medium, the student machine learning model having a primary loss function; receiving teacher scores provided by one or more analytic resources, the teacher scores being provided based on known results and behavior of pre-existing machine learning models used for accomplishing a first series of classification objectives; transforming the teacher scores into transformed teacher scores.