Cross-Modal Knowledge Transfer Using Batch-Norm Feature Matching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing cross-modal knowledge transfer methods require access to task-relevant paired data or source data, which is not feasible due to memory or privacy concerns, especially when adapting from one modality to another without labeled data.
Innovation Solution
The SOCKET framework uses task-irrelevant paired data and batch-norm statistics to bridge the modality gap by matching feature distributions, employing modality-specific and modality-agnostic loss functions to train target models without access to source data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If cross-modal knowledge transfer is performed using traditional methods requiring task-relevant paired source data, then knowledge transfer performance is improved, but data storage requirements increase and privacy issues arise
Solution Approach 1:
The patent extracts only the essential knowledge representations (feature distributions, batch normalization statistics, and task-irrelevant paired data) from the source domain, rather than transferring the entire source dataset. This extraction approach maintains knowledge transfer effectiveness while eliminating the need to store large amounts of source data, directly resolving the contradiction between performance and storage requirements
Solution Approach 2:
The patent introduces task-irrelevant paired data as an intermediary element that bridges the source and target modalities. This intermediary enables the learning of modality-invariant feature representations without requiring access to task-relevant source data, thus achieving knowledge transfer without increasing data storage requirements
2Quantity of substance
If cross-modal knowledge transfer is performed without access to source data, then privacy concerns are addressed and storage requirements are reduced, but knowledge transfer performance deteriorates
Solution Approach 1:
The patent performs preliminary actions during the source model training phase by capturing and storing batch normalization statistics and feature distributions. These pre-extracted statistical representations serve as compact knowledge proxies that can be transferred without requiring access to the actual source data, thus maintaining performance while reducing storage requirements
Solution Approach 2:
The patent changes the representation parameters from raw data to statistical features (means, variances, batch normalization parameters). This parameter transformation enables knowledge transfer using only compact statistical summaries rather than full datasets, resolving the performance-storage contradiction
3Adaptability or versatility
If task-irrelevant paired data is used to reduce modality gap, then adaptation effectiveness is improved, but system complexity increases
Solution Approach 1:
The patent segments the knowledge transfer process into distinct components: (1) learning with task-irrelevant paired data to reduce modality gap, (2) matching feature distributions using batch normalization statistics, and (3) fine-tuning on target data. This segmentation makes the complex adaptation process more manageable and interpretable, reducing perceived system complexity while maintaining effectiveness
Data Source
AI summary
A cross-modality knowledge transfer system is provided for adapting one or more source model networks to one or more target model networks. The system is configured to perform steps of providing the TI paired datasets through the source feature encoders of the one or more source model networks, extracting TI source features and TI source moments from the TI paired data by the BN layers of the one or more source model networks, providing the TI paired datasets and the unlabeled TR datasets through the one or more target model networks to extract TI target features and TR target moments, training jointly all the feature encoders of the one or more target model networks by matching the extracted TI target features and TR target moments with the TI source features and TI source moments along with mixing weights, and forming a final target model network by combining the trained one or more target model networks.