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Oct, 2023
可证明的物体为中心学习的组合概括
Provable Compositional Generalization for Object-Centric Learning
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Thaddäus Wiedemer, Jack Brady, Alexander Panfilov, Attila Juhos, Matthias Bethge...
TL;DR
通过可识别性理论的视角,我们研究了何时可以保证物体中心表示在组合泛化中保证可补全一致性,通过合成图像数据的实验验证了我们的理论结果和假设的实践相关性。
Abstract
Learning representations that generalize to novel compositions of known concepts is crucial for bridging the gap between human and machine
perception
. One prominent effort is learning
object-centric representations
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