Modality-Focused Encoder Attribute Removal
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Solution Overview
Problem
Existing attribute removal techniques are ineffective in multi-modality datasets due to oversimplified assumptions of independent modalities, leading to attribute leakage and poor performance.
Innovation Solution
A method using modality-focused encoders trained with removal and retention losses to remove uninterested attributes from multi-modality data, while retaining interested attributes, utilizing cosine distance and L2 norm losses, and pre-training reidentification classifiers with cross-entropy loss and SGD.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If existing attribute removal techniques are applied to multi-modality datasets, then the processing is simpler, but attribute leakage occurs and performance deteriorates
Solution Approach 1:
The patent divides the multi-modality data processing into separate modality-specific encoders (e.g., visual encoder, audio encoder, text encoder). Each encoder is independently trained to remove attributes from its specific modality while preserving modality-specific characteristics. This segmentation prevents attribute leakage across modalities while maintaining effective attribute removal.
Solution Approach 2:
The patent applies different processing strategies to different modalities based on their local characteristics. Each modality receives customized loss functions and encoder architectures suited to its specific data type, rather than applying a uniform processing approach. This local quality approach ensures optimal attribute removal for each modality type.
2Reliability
If modality-focused encoders are trained with removal and retention losses, then attribute removal effectiveness improves, but training complexity increases
Solution Approach 1:
The patent pre-trains modality-specific encoders and attribute classifiers before the main attribute removal training. This preliminary action establishes baseline representations for each modality and attribute type, making the subsequent joint training more efficient and reducing overall training complexity despite the sophisticated loss functions used.
Solution Approach 2:
The patent implements feedback mechanisms through the removal loss and retention loss functions. The removal loss provides feedback on how well attributes are being removed, while the retention loss provides feedback on how well useful information is being preserved. This dual feedback system guides the training process efficiently toward the desired outcome.
3Reliability
If attributes are removed from multi-modality data, then attribute leakage is prevented, but data utility for downstream tasks may be reduced
Solution Approach 1:
The patent changes the parameter optimization objectives by using dual loss functions: removal loss to minimize attribute information and retention loss to maximize useful information. This parameter change approach allows simultaneous achievement of attribute removal and utility preservation through careful loss function design and balancing.
Solution Approach 2:
The patent preserves modality-specific local qualities by using separate encoders for each modality. Each encoder maintains the unique characteristics and useful information of its modality while removing attributes. This local quality preservation ensures that downstream tasks can still leverage modality-specific useful information.
Data Source
AI summary
A method for removing uninterested attributes from multi-modality data may include: receiving, by a multi-modality attribute removal computer program executed by an electronic device, multi-modality data comprising a plurality of modalities from a data source, wherein data in each modality are related; receiving, by the multi-modality attribute removal computer program, an uninterested attribute in the multi-modality data to remove; training, by the multi-modality attribute removal computer program, a modality-focused encoder for each modality of the multi-modality data to remove the uninterested attribute using a removal loss and a retention loss for the respective modality; receiving, by the multi-modality attribute removal computer program, a multi-modality data set for processing; and processing, by the multi-modality attribute removal computer program, the multi-modality data set using the modality-focused encoders, wherein the processing results in a processed multi-modality data set with the uninterested attribute removed and one or more interested attribute retained.