Identity Embedding Adaptation for Unlabeled Target Domains
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Solution Overview
Problem
Existing AI models face significant performance degradation when deployed in target domains with different data characteristics from the source domain, particularly in identity verification systems, due to domain gaps, and existing adaptation methods require access to source data or target labels, which is often infeasible.
Innovation Solution
The IDentity-based Test-Time Adaptation (ID-TTA) method adapts AI models directly in the embedding space using a self-supervised adaptation module for consistency between original and augmented samples, and a pair-wise adaptation module to refine embedding distributions based on similarity assessments, without relying on source data or target labels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing adaptation methods are used to address domain gap, then model performance can be maintained, but access to source data or target labels is required which is often infeasible
Solution Approach 1:
The system performs self-supervised adaptation using only target domain unlabeled data. The model automatically adjusts its embedding space by computing consistency losses between augmented views of the same samples and pairwise losses between different samples, without requiring external labels or source data. This self-service mechanism enables the model to adapt to new domains independently.
Solution Approach 2:
The adaptation process is segmented into two distinct loss components: (1) consistency loss that operates on individual samples by comparing augmented views, and (2) pairwise loss that operates on sample pairs to maintain discriminative boundaries. This segmentation allows each component to address specific aspects of domain adaptation without requiring labels.
2Ease of operation
If classifier-based adaptation methods are used, then adaptation can be guided by output probabilities, but classifier-free ID systems cannot apply these methods
Solution Approach 1:
Instead of using the classifier output to guide adaptation (conventional approach), the patent inverts the approach by using the embedding space itself as the adaptation target. The consistency loss ensures that augmented views map to similar embedding positions, while the pairwise loss ensures that different samples maintain appropriate distances. This inversion eliminates dependency on classifier heads while providing explicit adaptation guidance through geometric constraints in embedding space.
3Ease of manufacture
If pre-trained models are deployed in target domains with different data characteristics, then deployment is straightforward, but significant performance degradation occurs due to domain gap
Solution Approach 1:
The system transitions from a static pre-trained model to a dynamic adaptation process. Upon deployment in the target domain, the model dynamically adjusts its embedding space by computing losses on target domain data and updating parameters accordingly. This dynamic adjustment allows the model to adapt to domain-specific characteristics while maintaining the simplicity of direct deployment, as no complex retraining or data collection is needed.
Data Source
AI summary
A method for controlling an artificial intelligence (AI) device can include obtaining a pre-trained AI model configured to generate embeddings from input data, receiving unlabeled target data from a target domain different than a source domain used to train the pre-trained AI model, determining first parameter updates for the pre-trained AI model by performing a self-supervised adaptation process based on a correlation between a first input sample and an augmented version of the first input sample, and generating an updated AI model based on the first parameter updates. Also, the method can further include determining second parameter updates by performing a pair-wise adaptation process based on adjusting embedding representations of a pair of input samples based on a threshold to correspond to a same identity, and generating a final adapted AI model based on the second parameter updates, the final adapted AI model being adapted to the target domain.