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Award ID contains: 1925101

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  1. ; ; (, Statistica Sinica)
    Full Text Available 
  2. ; ; ; (, NeurIPS)
    Accepted Manuscript Full Text Available
  3. ; ; (, SIAM Journal on Mathematics of Data Science)
    Full Text Available 
  4. ; ; ; (, Journal of machine learning research)
    Accepted Manuscript Full Text Available
  5. ; ; ; ; (, IEEE Transactions on Geoscience and Remote Sensing)
  6. ; ; (, IEEE Statistical Signal Processing Workshop)
    Full Text Available 
  7. ; ; ; ; (, IEEE Computer Society Conference on Computer Vision and Pattern Recognition)
    null (Ed.)
    We study image segmentation from an information-theoretic perspective, proposing a novel adversarial method that performs unsupervised segmentation by partitioning images into maximally independent sets. More specifically, we group image pixels into foreground and background, with the goal of minimizing predictability of one set from the other. An easily computed loss drives a greedy search process to maximize inpainting error over these partitions. Our method does not involve training deep networks, is computationally cheap, class-agnostic, and even applicable in isolation to a single unlabeled image. Experiments demonstrate that it achieves a new state-of-the-art in unsupervised segmentation quality, while being substantially faster and more general than competing approaches. 
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    Accepted Manuscript Full Text Available
  8. ; ; (, IEEE Transactions on Computational Imaging)
    null (Ed.)
    Full Text Available 
  9. ; ; ; (, Proceedings of Machine Learning Research)
    null (Ed.)
    Accepted Manuscript Full Text Available
  10. ; ; ; ; (, International Conference on Artificial Intelligence and Statistics)
    Accepted Manuscript Full Text Available