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1706.04313.pdf
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🔗 Source: arxiv.org
📊 Size: 9.43 MB
📄 Pages: 10 pages
⬇️ Downloads: 47

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Convolutional neural networks (CNNs) have shown great success in computer vision, but typically do not exhibit compositionality, where the representation of the whole is composed of the representation of its parts. This paper proposes a method for augmenting and training CNNs to learn compositional features, which encourages networks to form representations that disentangle objects from their surroundings and from each other, promoting better generalization. The method is agnostic to the specific details of the underlying CNN and can be used with any CNN. Experiments show that the learned representations lead to feature activations that are more localized and improve performance over non-compositional baselines in object recognition tasks.

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Convolutional neural networks (CNNs) have shown great success in computer vision, but typically do not exhibit compositionality, where the representation of...

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  • File Format: PDF
  • File Size: 9.43 MB
  • Pages: 10
  • Language: EN
  • Total Downloads: 47
  • Last Updated: 2 weeks ago

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