Image transformation with a hybrid autoencoder and generative adversarial network machine learning architecture
US-10803347-B2 · Oct 13, 2020 · US
Learning an autoencoder
US11468268B2 · US · B2
| Field | Value |
|---|---|
| Publication number | US-11468268-B2 |
| Application number | US-202016879507-A |
| Country | US |
| Kind code | B2 |
| Filing date | May 20, 2020 |
| Priority date | Apr 27, 2017 |
| Publication date | Oct 11, 2022 |
| Grant date | Oct 11, 2022 |
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Abstract
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A computer-implemented method for learning an autoencoder notably is provided. The method includes obtaining a dataset of images. Each image includes a respective object representation. The method also includes learning the autoencoder based on the dataset. The learning includes minimization of a reconstruction loss. The reconstruction loss includes a term that penalizes a distance for each respective image. The penalized distance is between the result of applying the autoencoder to the respective image and the set of results of applying at least part of a group of transformations to the object representation of the respective image. Such a method provides an improved solution to learn an autoencoder.
First claim
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The invention claimed is: 1. A computer-implemented method for learning an autoencoder, the method comprising: obtaining a dataset of images, each image including a respective object representation, wherein the object representations are each a representation of an instance of a same class of objects; and learning the autoencoder (g W′ ∘f W ) based on the dataset, the learning including minimization of a reconstruction loss (E(W,W′)), the reconstruction loss including a term ( ∑ i = 1 n inf h ∈ 𝒢 d ( g W ′ ∘ f W ( x i ) , h ∘ x i ) , ∑ i = 1 n inf h ∈ 𝒢 ( d ( g W ′ ∘ f W ( x i ) , h ∘ x i ) + ρ ( h ) ) , ∑ i = 1 n inf h ∈ 𝒢 d ( g W ′ ∘ f W ( x i _ ) , h ∘ x i ) ) that penalizes for each respective image (x i ) a distance ( d q ( g W ′ ∘ f W ( x i ) , x i _ ) = inf h ∈ 𝒢 d ( g W ′ ∘ f W
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Validation; Performance evaluation · CPC title
Classification techniques · CPC title
using neural networks · CPC title
Validation; Performance evaluation; Active pattern learning techniques · CPC title
- G06T7/30Primary
Determination of transform parameters for the alignment of images, i.e. image registration · CPC title
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- What does patent US11468268B2 cover?
- A computer-implemented method for learning an autoencoder notably is provided. The method includes obtaining a dataset of images. Each image includes a respective object representation. The method also includes learning the autoencoder based on the dataset. The learning includes minimization of a reconstruction loss. The reconstruction loss includes a term that penalizes a distance for each res…
- Who is the assignee on this patent?
- Dassault Systemes
- What technology area does this patent fall under?
- Primary CPC classification G06T7/30. Mapped technology areas include Physics.
- When was this patent published?
- Publication date Tue Oct 11 2022 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).