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[其他] AI generates abstract diagrams of IQ tests as good as 10th grade student

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Ange1浮夸 发表于 2016-10-1 05:59:01
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Contextual RNN-GANs for Abstract Reasoning Diagram Generation

    Arnab Ghosh * , Viveka Kulharia * , Amitabha Mukerjee, Vinay Namboodiri, Mohit Bansal
    * Equal contribution
   Motivation

   
       
  • Understanding, predicting, and generating object motions and transformations is a core problem in artificial intelligence.
       
  • Modeling sequences of evolving images may provide better representations and models of motion and may ultimately be used for forecasting, simulation, or video generation.
       
  • Diagrammatic Abstract Reasoning is an avenue in which diagrams evolve in complex patterns and one needs to infer the underlying pattern sequence and generate the next image in the sequence.
       
   An Example with an Explanation

   

AI generates abstract diagrams of IQ tests as good as 10th grade student-1 (generation,abstract,generate,sequence,complex)

   An explanation of the ground truth is that the dashed line first goes to the left, then to the right, and then on both sides, and also changes from single to double, hence the ground truth should have double dashed lines on both the sides. On the corners, the number of slanted lines increase by one after every two images, hence the ground truth should have four slant lines on both the corners.
   Some More Example Problems From DAT-DAR Dataset

   


AI generates abstract diagrams of IQ tests as good as 10th grade student-2 (generation,abstract,generate,sequence,complex)

   The Model

   Contextual RNN-GAN

   
       
  • GANs have been shown to be useful in several image generation and manipulation tasks and hence it was a natural choice to prevent the model make fuzzy generations.
       
  • In Context-RNN-GAN, 'context' refers to the adversary receiving previous images (modeled as an RNN) and the generator is also an RNN. The name distinguishes it from our simpler RNN GAN model where the adversary is not contextual (as it only uses a single image) and only the generator is an RNN.
       
  • The discriminator is modeled as a GRU-RNN which gets all the preceding images to decide whether the generation by the Generator is the correct image for the timestep.
       
  • The generator is modeled as a GRU-RNN which tries to generate an image using the preceding images. It is guided by the contextual discriminator to produce real looking images.
       
   

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李明金 发表于 2016-10-1 07:25:16
楼下的,觉得比特币咋样啊?
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黄瑞琦 发表于 2016-10-1 17:40:16
那些路人甲乙丙丁在年生散场的剧场里将五彩纷呈和苍白无力潇洒的演绎。
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7548629 发表于 2016-10-1 17:59:54
能力就像瓜子仁,只有咬牙才能嗑出来。
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语嫣 发表于 2016-10-1 19:05:25
有空一起交流一下
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wzb1518 发表于 2016-10-2 06:56:34
秀起来~
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hwsfn 发表于 2016-10-3 02:46:34
楼下是射手
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anyword 发表于 2016-10-8 12:27:46
呵呵。。。
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回憶〤亂人心 发表于 2016-10-8 16:47:37
一语道尽爱情的残忍。情到深处人孤。疾苦使人成熟,强项的人会感悟爱的真谛,而懦弱的人徒生怅恨。
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一朵奇葩向阳开 发表于 2016-10-21 03:24:30
失而复得的东西。永远都是二手货。
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