Method and system for directed transfer of cross-domain data based on high-resolution remote sensing images
US-11741572-B2 · Aug 29, 2023 · US
Method of unsupervised domain adaptation in ordinal regression
US12217484B2 · US · B2
| Field | Value |
|---|---|
| Publication number | US-12217484-B2 |
| Application number | US-202217737114-A |
| Country | US |
| Kind code | B2 |
| Filing date | May 5, 2022 |
| Priority date | Dec 16, 2021 |
| Publication date | Feb 4, 2025 |
| Grant date | Feb 4, 2025 |
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Abstract
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A method of jointly training of a transferable feature extractor network, an ordinal regressor network, and an order classifier network in an ordinal regression unsupervised domain adaption network by providing a source of labeled source images and unlabeled target images; outputting image representations from a transferable feature extractor network by performing a minimax optimization procedure on the source of labeled source images and unlabeled target images; training a domain discriminator network, using the image representations from the transferable feature extractor network, to distinguish between source images and target images; training an ordinal regressor network using a full set of source images from the transferable feature extractor network; and training an order classifier network using a full set of source images from said transferable feature extractor network.
First claim
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What is claimed is: 1. An ordinal regression unsupervised domain adaption network for jointly training of a transferable feature extractor network, an ordinal regressor network, and an order classifier network, comprising: a source of labeled source images and unlabeled target images; a transferable feature extractor network, operatively connected to said source of labeled source images and unlabeled target images, to output image representations, said image representations being realized by a minimax optimization procedure; a domain discriminator network operatively connected to said transferable feature extractor network; an ordinal regressor network operatively connected to said transferable feature extractor network; and an order classifier network operatively connected to said transferable feature extractor network and said domain discriminator network; said domain discriminator network being trained, using said image representations from said transferable feature extractor network, to distinguish between source images and target images; said ordinal regressor network being trained, using a full set of source images from said transferable feature extractor network; said order classifier network being trained, using a pair of source images from said transferable feature extractor network. 2. The ordinal regression unsupervised domain adaption network as claimed in claim 1 , wherein said transferable feature extractor network is trained by maximizing a loss of said domain discriminator. 3. The ordinal regression unsupervised domain adaption network as claimed in claim 1 , wherein a total loss for training the ordinal regression universal domain adaptation network is given as: ℒ ( F , G r , G o , G d ) = ℒ or ( F , G r ) + γ 1 ℒ ord ( F , G o ) + γ 2 ℒ dom ( F , G d ) , γ 1 , γ 2 are hyper-parameters controlling an importance of order and domain discrimination adversarial losses; wherein F ⋆ , G r * , G o * = arg min F , G r , G o max G d ℒ ( F , G r , G o , G d ) is solved by alternating between optimizing F, G r , G o , and G d until the total loss converges. 4. The ordinal regression unsupervised domain adaption network as claimed in claim 3 , wherein the loss for said ordinal regressor network is defined on labeled source images, ℒ or ( F , G o ) = 𝔼 x i ∈ D s L coral ( G r ( F ( x i ) ) , y i ) , where
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Incorporation of unlabelled data, e.g. multiple instance learning [MIL] · CPC title
the supervisor being an automated module, e.g. "intelligent oracle" · CPC title
using neural networks · CPC title
based on specific statistical tests · CPC title
- G06V10/7715Primary
Feature extraction, e.g. by transforming the feature space, e.g. multi-dimensional scaling [MDS]; Mappings, e.g. subspace methods · CPC title
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- What does patent US12217484B2 cover?
- A method of jointly training of a transferable feature extractor network, an ordinal regressor network, and an order classifier network in an ordinal regression unsupervised domain adaption network by providing a source of labeled source images and unlabeled target images; outputting image representations from a transferable feature extractor network by performing a minimax optimization procedu…
- Who is the assignee on this patent?
- Naver Corp
- What technology area does this patent fall under?
- Primary CPC classification G06V10/7715. Mapped technology areas include Physics.
- When was this patent published?
- Publication date Tue Feb 04 2025 00:00:00 GMT+0000 (Coordinated Universal Time) (B2). Legal status and post-grant events are not shown on this page.
- What related patents are in patentsdb?
- We list 6 related publications on this page (citations in our corpus or others sharing the same primary CPC).