Unsupervised Domain Adaptation via Pretrained Classifier Augmentation
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Solution Overview
Problem
Existing domain adaptation methods assume access to labeled source data, which is often unavailable due to technical or legal constraints, making it challenging to adapt models from a source domain to a target domain without labeled target data.
Innovation Solution
A system and method that uses a pretrained classifier to generate class label predictions for target samples, augments feature representations with these predictions, and employs Stacked Marginalized Denoising Autoencoders (SMDA) to learn a transformation that minimizes reconstruction error, enabling unsupervised domain adaptation without access to source data or labels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If domain adaptation methods assume access to labeled source data, then classification accuracy can be improved, but data availability and privacy constraints are violated
Solution Approach 1:
The patent extracts only the essential information from source data through a pretrained classifier, rather than requiring access to the actual source data. The classifier serves as a compressed representation that captures domain-specific patterns without needing the original training examples, thus resolving the contradiction between achieving accurate classification and maintaining data unavailability/privacy.
Solution Approach 2:
The pretrained classifier acts as an intermediary between the source domain and target domain. Instead of directly transferring source data or labels, the classifier mediates the adaptation process by providing predictions that guide the learning of domain-specific features in the target domain, enabling accurate classification without access to source data.
2Reliability
If source data is stored and accessed for domain adaptation, then model transfer performance improves, but technical and legal constraints are violated
Solution Approach 1:
The method extracts the essential knowledge from source data into a pretrained classifier model, eliminating the need to store or access actual source data. This extraction approach maintains model transfer performance while complying with technical and legal constraints that prohibit storing or accessing source data.
Solution Approach 2:
Instead of copying or storing source data, the patent creates a functional copy through the pretrained classifier that encapsulates the source domain knowledge. This classifier copy enables model transfer without requiring the original source data to be stored or accessed, resolving the compliance issue.
3Object-affected harmful factors
If traditional domain adaptation methods are used without source data, then privacy concerns are addressed, but classification accuracy deteriorates
Solution Approach 1:
The patent performs preliminary action by training a classifier on source data before the adaptation phase. This pretrained classifier captures domain-specific patterns in advance, allowing the target domain adaptation to proceed without accessing source data while still benefiting from the pre-learned domain knowledge, thus maintaining accuracy while protecting privacy.
Solution Approach 2:
The method changes the parameter representation from raw source data to classifier predictions. By transforming the source domain information into classifier output probabilities or scores, the system preserves the essential discriminative information needed for accurate classification while removing the actual source data that would compromise privacy.
4Measurement precision
If labeled target data is available for adaptation, then classification performance improves, but computational costs and complexity increase
Solution Approach 1:
The patent applies partial action by using only the predictions from the pretrained classifier on target data, rather than requiring full labeled target datasets. This partial supervision approach achieves good classification performance while reducing the computational complexity associated with processing and labeling extensive target data.
Data Source
AI summary
A classification method includes receiving a collection of samples, each sample comprising a multidimensional feature representation. A class label prediction for each sample in the collection is generated with one or more pretrained classifiers. For at least one iteration, each multidimensional feature representation is augmented with a respective class label prediction to form an augmented representation, a set of corrupted samples is generated from the augmented representations, and a transformation that minimizes a reconstruction error for the set of corrupted samples is learned. An adapted class label prediction for at least one of the samples in the collection is generated using the learned transformation and information is output, based on the adapted class label prediction. The method is useful in predicting labels for target samples where there is no access to source domain samples that are used to train the classifier and no access to target domain training data.