METHOD AND SYSTEM OF ADAPTING AN INITIAL MODEL OF A NEURAL NETWORK, Storage part and vehicle

A method of adapting an initial model trained with a marked image of a source domain to an adaptive model includes: copying the initial model into the adaptive model; dividing the adaptation model into an encoder portion and a second portion, wherein the second portion is configured to process featu...

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Bibliographic Details
Main Authors OTHMEZOURI GABRIEL, ERKENT OZGUR, LAUGIER CHRISTIAN
Format Patent
LanguageChinese
English
Published 31.08.2021
Subjects
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
PHYSICS
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Summary:A method of adapting an initial model trained with a marked image of a source domain to an adaptive model includes: copying the initial model into the adaptive model; dividing the adaptation model into an encoder portion and a second portion, wherein the second portion is configured to process features output from the encoder portion; adapting the adaptation model to the target domain using the images (xs) of the source domain and the target domain while fixing the parameters of the second portion and minimizing a function of the following two distances: the distance between the initial model and the feature of the source domain of the output of the encoder of the adaptation model; and measuring a distance of a distribution distance between probabilities of features obtained for the image of the source domain and the image of the target domain. 将利用源域的经标记的图像训练的初始模型适配到适配模型的方法，其包括：将初始模型复制到适配模型中；将适配模型划分成编码器部分和第二部分，其中，该第二部分配置为处理从所述编码器部分输出的特征；在固定所述第二部分的参数并使以下两个距离的函数最小化的同时使用源域和目标域的图像(xs)将所述适配模型适配到目标域：初始模型和适配模型的编码器的输
Bibliography:Application Number: CN202110220777